VaseVQA-3D: Benchmarking 3D VLMs on Ancient Greek Pottery
Authors: Nonghai Zhang
Zeyu Zhang
Jiazi Wang
Yang Zhao
Hao Tang
Venue: ICLR 2026 Poster (2026)
Keywords: Vision-Language Models
Vision Question Answering
Ancient Greek Pottery
Cultural Heritage
Dataset Construction
3D Generation
Archaeological AI
Multimodal Learning
Primary area: datasets and benchmarks
TL;DR: We introduce VaseVQA-3D, the first 3D vision question answering dataset for ancient Greek pottery, along with specialized vision-language models trained using reinforcement learning with verifiable rewards.

Abstract: Vision-Language Models (VLMs) have achieved significant progress in multimodal understanding tasks, demonstrating strong capabilities particularly in general tasks such as image captioning and visual reasoning. However, when dealing with specialized cultural heritage domains like 3D vase artifacts, existing models face severe data scarcity issues and insufficient domain knowledge limitations. Due to the lack of targeted training data, current VLMs struggle to effectively handle such culturally significant specialized tasks. To address these challenges, we propose the VaseVQA-3D dataset, which serves as the first 3D visual question answering dataset for ancient Greek pottery analysis, collecting 664 ancient Greek vase 3D models with corresponding question-answer data and establishing a complete data construction pipeline. We further develop the VaseVLM model, enhancing model performance in vase artifact analysis through domain-adaptive training. Experimental results validate the effectiveness of our approach, where our VaseVLM-7B-RL achieves 12.8\% improvement in R@1 accuracy and 6.6\% improvement in lexical similarity compared to the strongest baselines on the VaseVQA-3D dataset, significantly improving the recognition and understanding of 3D vase artifacts, providing new technical pathways for digital heritage preservation research.

VQA33D Benchmarking 3D VLMs on Ancient Greek Pottery
VASEVQA-3D: BENCHMARKING 3D VLMS ON ANCIENT GREEK POTTERY
Nonghai Zhang Zeyu Zhang
Jiazi Wang?*
Yang Zhao Hao Tang
1 School of Computer Science, Peking University
Schooo of Computer Science and Technology, Beijing Jiaotong University
LLa Trobe University
Equal contribution. Prooecct lead.
*Corresponding author: bjdxtanghao@ gmail.com
ABSTRACT
Vision-Language Models (VLMs) have achieved significant progress in mul- timodal understanding tasks, demonstrating strong capabilities particularly in general tasks such as image captioning and visual reasoning. However, when dealing with specialized cultural heritage domains like 3D vase artifacts, exist- ing models face severe data scarcity issues and insufficient domain knowledge limitations. Due to the lack of targeted training data, current VLMs strug- gle effectively handle such culturally significant specialized tasks. To ad- dress these challenges, we propose the VaseVQA-3D dataset, which serves as the first 3D visual question answering dataset for ancient Greek pottery analy- sis, collecting 664 ancient Greek vase 3D models with corresponding question- answer data and establishing a complete data construction pipeline. We fur- ther develop the VLse model, enhancing model performance in vase ar- tifact analysis through domain-adaptive training. Experimental results vali- date the effectiveness of our approach, where our VaseVLMM77B-RI. achieves 12.8% improvement in R@ I accuracy and 6.6% improvement lexical sim- ilarity compared the strongest baselines the VaseVQA-3D dataset, sig- nificantly improving the recognition and understanding of 3D vase artifacts, providing new technical pathways for digital heritage preservation research. Website:
INTRODUCTION
A picture is worth a thousand words. Vision-Language Models (VLMs) have demonstrated re- markable capabilities in understanding and reasoning about visual content, achieving impressive performance across various multimodal tasks such Visual Question Answering (VQA), image captioning, and visual reasoning tasks (Liu et al. [023a; Deitke al. (025). These models have shown significant progress in bridging the gap between visual perception and language understand- ing, performing excellently in general-scenario visual question answering tasks and providing new possibilities for handling complex visual understanding tasks. However, existing VLMs face two
wayther Sif--oot eco with
daplctngg woRie and C-s
burliing De de Berry Kalf
Morann herry seitts Matioral
Chanvi Norif Bette
Figure 1: Captions in Vase VQA-3D dataset. Each GLB-format 3D vase rendered four canon- ical fows-ffrot, back, top, and bottom-and accompanied by concise caption that records decorative motifs, manufacturing technique, provenance, and current repository

---

VaseVQA-3D: Benchmarking 3D VLMs on Ancient Greek Pottery
significant challenges when dealing with specialized domains like 3D vase artifacts: (1)Ancient Greek pottery, as a crucial carrier of Hellenic civilization with over 2,000 years of historical sig- nificance and more than 300 years of systematic archaeological research, represents rare long-tail data for VLMs, yet existing datasets lack comprehensive 3D representations of these artifacts, and (2) due to data scarcity, no current VLMS handle such specialized tasks competently, yet this domain is crucial for digital heritage preservation and cultural heritage transmission, creating an urgent need for specialized vase-domain VLMs.
To address the challenge of 3D vase artifact data scarcity, we need to construct specialized 3D vase datasets and benchmarks that can systematically assess model performance on 3D cultural heritage objects. Specifically, ancient Greek pottery requires 3D understanding because its archaeological significance lies in spatial features (symmetry, proportions, morphology) and complete geomet- ric representation, which cannot be fully captured through fragmented 2D views. To address the challenge of insufficient model expertise, we need to develop VLMs with vase-domain expertise, enhancing model capabilities in archaeological analysis tasks through domain-adaptive training.
We introduce Vase VQA-3DD specialized dataset for ancient Greek pottery visual question answer- ing, addressing the data gap in this professional domain. Our question-answer pairs are structured around six archaeological semantic dimensions (Fabric, Technique, Shape, Dating, Decoration, At- tribution), reflecting professional archacological knowledge rather than generic visual attributes. We propose a comprehensive pipeline for transforming existing 2D vase images into high-fidelity 3D) representations, including rigorous data filtering, 2D-to-3D conversion techniques, and large model enhancement of existing vase QA data. We collect 24 high-quality real GLB models as Eval to systematically evaluate the effectiveness of our data synthesis pipeline. Based on the validated high-quality data, we develop Vase a VLM specifically fine-tuned for 3D vase understand- ing capabilities through comain-adaptivy training strategies and archaeological expertise integra- tion. We conduct extensive evaluations on multiple state-of-the art VLMs, and experimental results demonstrate that our fine-tuned model can achieve better recognition and understanding of 3D vase data, validating the effectiveness of our approach in 3D vase and other cultural heritage domains, providing new technical perspectives and solutions for digital heritage preservation.
In summary, the contributions of our paper can be summarized in three folds:
We introduce VaseVQA-3D, a comprehensive dataset for evaluating VLMs on 3D ancient pot- tery, including 664 high-quality 3D vase models and diverse question-answer pairs exploring vase attribute information. We also construct VaseEval for evaluating 3D asset quality, filling the data gap in this professional domain.
We propose VLM, a vision-language model specifically fine-tuned for 3D vase understand- ing. Since 3D vase artifacts are and constitute long-tail data, existing VLMs struggle with such specialized tasks. Our VaseVLM employs a two-stage training approach: first establishing baseline performance through LoRA-based supervised fine-tuning (SFT) on 360-degree rotation videos and archaeological captions, then applying GRPO reinforcement learning with our novel Reinforcement Learning with Verifiable Rewards (RLVR) framework that decomposes archaeo- logical descriptions into six semantic dimensions (Fabric, Technique, Shape, Dating, Decoration, Autribution) for dimensiiimnsionll reward computation and quality control.
We conduct comprehensive experimental evaluation demonstrating significant improvements in archaeological VQA tasks. Our VLscVLL-7BBRR RL achieves 12.8% improvement in R@ accuracy and 6.6% improvement in lexical similarity compared to the strongest baselines on the Vase 3D dataset. Beyond technical contributions, our work provides meaningful exploration for AI applications in cultural heritage preservation, offering new pathways for digital heritage analysis and interdisciplinary collaboration between computer science and archaeology.
We conduct comprehensive experimental evaluation with significant social impact, validating the effectiveness of our approach through extensive experiments, while our work has important so- cial value in digital heritage preservation, providing meaningful exploration for AI technology applications in cultural heritage protection.
RELATED WORK
VLMs and Visual Question Answering. VLMs serve as core technology in multimodal AL, en- abling machines to understand and reason about visual content through natural language. Modern
2

---

VQseVD Benchmarking 3D VLMs on Ancient Greek Pottery
Uuery LiWau the time
Query 1:Weed titen
Query, W.l x wchtigee the
Query 2 2 is the tedhergeeg gr of vosn!
Axored 2 txceaiue the vae
FIGURE
Anowrr 2 see sechaage waw RRDD PAOL
dight with chaise. somorose conoroienblee sabris durit devics eakle, dinyyos
Asswor kills deoamtee ofe
Auswer The Jooriion nac luay: weena in ciliiee tendr).
drraing bom betwonn anys, one with witakkn
Black-Figure Amphora, 550-500 BUE, depicting warnoes and Dionysian reveley; MFA Boston.
Captiom:
Caption: squat Adhenian figure lekythos, c.
depicting
a woman's head in sakkos amid sendrils Brussels Museum
Figure 2: QA in Vase VQs-VD dataset. Each data entry contains high-quality 3D vase models, structured question-anse wnsser pairs, and GPT-4o enhanced descriptive captions, providing comprehen- sive support for multimodal understanding of ancient Greek pottery.
original images unlabelled images
ResNet-50 50 classifier What a good Image?
high-quality images
Images:passed
Clip Flitering
Final Images
Fitteiigg fragments))
Filterl
Filter2
Example Learning
24 Po.
Dest
Figure 3: Complete Data Quality Filtering Pipeline. The figure shows our comprehensive filter- ing methodology, including ResNet-50-basee quality assessment for removing low-quality images, followed by dual CLIP-based semantic filtering for fragment removal and optimal image selection.
VLM development grounded in contrastive learning, with CLIP (Radford et al. 2021 having pioneered large-scale visual-text alignment. Recent large VLMs have significantly enhanced sual understanding capabilities. Closed-source models like GPT-4V (Hurst et al.) 2024) and Gem- ini (Comanici et al., 2025), and open-source models such as Qwen2.5-VL (Bai al. 2025 and
Intern (Chen et al. 2002) have demonstrated remarkable performance across various multimodal tasks. Specialized techniques have emerged in 3D vision-language understanding: Cap3D (Luo)
ee al. (023), DiffuRank (Luo et al.] (2024), and LLaVA-3D (Zhu et al. 2024) achieved advances in 31D model descriptions and greestion-answeriie
3D Generation and Reconstruction. In image-to-3D model generation, DreamFusion (Poole et al., 2022) pioneered the application of image diffusion priors to 3D generation. Recent advances include TripoSG (Li et al. 2025) and Hunyuan3D (Lai et al. 2025). which achieved state-of-the-an perfor- mance in 3D shape generation tasks through improved architectures and training strategies.
Cultural Heritage and Archaeological AI. AI technologies effectively enhancing cultural her- itage preservation. Recent works include ArchacoScape (Perron et al. (2024) for archacological site identification and automated restoration methods for cultural artifacts (Feng et al. 2025).
For detailed related work introduction, please refer to Appendix A.2
DATASET: VASEVQA-3D
Data Sources and Collection Our Vase VQA-3D dataset construction based on two main data sources: the large-scale 2D vase image collection and corresponding archaeological metadata pro- vided by the Vas (Ge et al.) 2025) dataset (as shown in Figure 5). and a curated set of high- quality 3D references drawn from the Sketchfab digital museum (as shown in Figure 4 The Va- seV seVQA dataset serves as our primary data source, containing over 30,000 2D images of ancient Greek vases, each accompanied by detailed archaeological metadata annotations covering six vase attributes: fabric composition, manufacturing techniques, morphological shapes, historical dat- ing, decorative elements, and artistic attribution. To validate our 3D generation pipeline quality and
3

---

VaseVQA-3D. Benchmarking 3D VLMs on Ancient Greek Pottery
Inputs
Ground Truth
Hunyuan3D
TripoS6
Figure 4: 3D Generation Methods Comparison. Comparison of TripoSG and Hunyuan3D generation effects based on the VaseEval validation set. TripoSG performs better in mesh quality, and although Hunyuan3D has advantages in texture mapping effects, TripoSG- models are closer to ground truth, thus selected for large-scale dataset construction.
perform generative model selection, we collected high-quality reference data from the Sketchfab digital museum construct our VaseEval validation set for 3D generation quality assessment.
Data Quality Filtering However, the original VaseVQA dataset suffers from significant quality issues, containing numerous vase fragments, blurred images, and even sketches, which severely impact the dataset's suitability for high-quality 3D generation tasks. To enhance dataset quality, designed three-stage progressive filtering framework, as shown in Figure First, we train ResNet-50 binary classifier based on manual annotations for preliminary quality screening, automat- ically identifying and removing low-quality images. Second, we employ CLIP models for fragment detection. using predefined text prompts to calculate the similarity difference between images and descriptions of complete vases versus fragments, adopting binary classification approach to auto- matically identify and remove vase fragments. Finally, addressing the multi multi-viewpoint issue for each vase, we use CL.PP to compute similarity scores between each viewpoint image and high-quality de- scriptive text, selecting the image with the highest score as the optimal representative view. This comprehensive filtering mechanism ensures the final dataset contains only high-quality, complete, and archaeologically representative vase images, providing a reliable foundation for subsequent 3D generation and VQA tasks.
VaseEval: 3D Generation Quality Assessment To ensure the quality and reliability of our 3D generation pipeline, we construct VaseEval(as shown in Figure ( a specialized validation set con- sisting of 24 high-quality ancient Greek vase GLB files carefully selected from the Sketchfab digital museum. These reference models serve as ground truth for evaluating the effectiveness of different 3D generation methods and validating our data synthesis pipeline.
VaseEval covers diverse vase morphologies including narrow-body vases, wide-mouth vessels, and various decorative patterns, providing comprehensive coverage for quality assessment. Each model in VascEval features clear geometric structures and rich textural details, enabling systematic evaluation of both mesh quality and texture fidelity in generated 3D models.
As shown in Figure 4
Through comparative analysis using VaseEval, validated our choice of 3D generation method and ensured the archaeological accuracy and visual quality of our final Vase dataset. VaseEval served as the benchmark for selecting the most suitable 3D generation method, ensuring that our chosen approach produces high-quality 3D models with accurate geometric representation and visual fidelity for ancient Greek pottery.
4

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
Filtening
30 Voies ALB Senerate
Evalution
Depert
Vasic enllation
Fitering
generation
Vast bou-ddiped
Exclttion Matrica
20 deaemitnn
Wenarn the
kenwon
Vase Training
VaseVQM-30
2Pd the ond 30 nor
Quntiill: vesa?
perto
Assoort The of fuberi ATHDN
Prowatt
Promptt
Promutt
vese?
Coption erdiatio
longaage
3b- Sb-Spesiidied
Qpertecd WWha shope nans SQUATT
Cpption deser briin
General-
shope
welio-loogueee weddss/LLV))
Figure 5: Complete Pipeline for Vase Dataset Construction. The pipeline progresses from initial data collection (30K+ images) through quality filtering (664 images), 3D generation (664 models), QA construction (4K+ pairs), to final model training. Each component includes specific quality control mechanisms and validation procedures.
Vase VQA33D Construction Based on the filtered high-quality 2D images, we designed a com- prehensive data synthesis pipeline that converts 2D images into high-fidelity 3D models while main- taining archaeological accuracy and cultural authenticity, as shown in Figure 5 Our data synthesis pipeline contains three core stages: first, filtering 3,880 high-quality 2D images from 30,000+ orig- inal images through ResNet-50 and CLIP dual filtering mechanisms; then, converting these 2D images into 664 high-fidelity 3D models using TripoSG technology; and finally, generating 4,460 structured question question-answer answer pairs and corresponding descriptive captions by cleaning and organizing the original archaeological metadata using GPT-4o (Hurst et al.) 2024).
To construct high-quality 3D vase models, we evaluated two currently recognized superior 2D-to- 3D generation methods: TripoSG and Hunyuan3ID, both of which excel in 2D-to-3ID reconstruction tasks. We used the VaseEval validation set to evaluate the effectiveness of both generation methods by capturing front-view photographs of these 3D models, as shown in Figure 4 From the analysis of the results in the figure, we found that TripoSG generated higher mesh quality, while Hunyuan3D produced better texture mapping effects. However, to restore the ground truth effect, we gener- ally believed that TripoSG generated more realistic results with better vase model quality, so ultimately chose TripoSG for large-scale data generation.
As shown in Figure 2 Our VQseVQ dataset comprises two complementary components. The structured VQA component directly adopts the original question-answer content from Vase covering six core vase attributes: Fabric, Technique, Shape, Dating, Decoration, and Attribution. Each question follows the standardized format "What is the [attribute] of the vase?" to ensure con- sistency and fairness in evaluation. The answers are derived from verified archaeological metadata, maintaining factual accuracy and scholarly reliability.
The caption component provides descriptive captions for each vase by organizing and cleaning the original archaeological metadata from the VQs dataset. The original metadata contains struc- tured information (e.g.. "Fabric: ATHENIAN; Technique:
Shape: LEKY :TOS:
Date: -525 to -475 BCE") but is often fragmented and noisy. We use GPT-4o to consolidate this existing archaeological information into coherent museum-style descriptions (e.g.. "Athenian black- figure lekythos, 525-475 BCE") without introducing new archaeological content. This cleaning process removes noise and improves readability while preserving the original archacological facts. The final Vase dataset contains GLB-forma 3D model files, with each model associated with corresponding structured question-answer pairs and cleaned descriptive captions, providing a solid foundation for training and evaluating VL.Ms in the cultural heritage domain.
5

---

VQseVO Benchmarking 3D VLMs on Ancient Greek Pottery
Entrence
Reward Function
Beliout
Question:eenrrtt casc end accurate coption. Trclate vessel type (eg- lekythes, unshers, hychaa) techwqqu (c., Mack-figunn, red probable find aedd-pppti dertifask..
Geserate and accurate
subject
caption
Ouiput
Ground Truth
Atkessan slack-figure lelyrha, indistinct desorsted
Afherian Black-Piprr Fanothandi Amplani, 525-475 RCC, Allons Kleephradis Poarier
Training
VLM
KL divergence
REF
Vanty Rexand
Verify. fabric
Scauis
Verify__2 Technique
Verify 3 shapp
Group
Firal
Verify 4 4-date
Repertton persity
|
Verify 5 5edecoration
Computation
Ofhi
Length pandity
Veniy Sottribbtiion
Reaand
strachudd consitency
Figure 6: Reinforcement Learning with Verifiable Rewards (RLVR) Framework. The figure shows our multi-dimensional reward computation system that evaluates archaeological descriptions across six semantic dimensions: Fabric, Technique, Shape, Dating, Decoration, and Attribution. The framework includes semantic similarity analysis, quality control penalties, and similarity rewards to ensure accurate and academically appropriate responses.
4 THE PROPOSED METHOD: VASEVLM
Overview This section introduces the comprehensive pipeline of our 3D visual question answer- ing system specifically designed for ancient Greek vase analysis, shown in Figure Our method contains several key components: initial data collection from large-scale 2D vase datasets, compre- hensive data quality filtering combining deep learning classifiers and CLIP-based methods, 3D gen- eration method validation and large-scale model generation, refining and organizing content based on original Vas QA data using GPT-4o to obtain concise and clear caption question-answer data to improve QA quality and construct the final VaseVQA-3D, dataset quality evaluation using multiple VLM baselines, and specialized model training using fine-tuning and reinforcement learn- ing.
Supervised Fine-Tuning (SFT) Based on the constructed Vase dataset, we further trained specialized ancient Greek vase analysis model VLM and evaluated the dataset's perfor- mance across different VLMs. We adopt Qwen2.5-VL. (3B and 7B variants) as the base model, with training inputs comprising 360-degree rotation videos generated from GLB files and corresponding refined caption descriptions that contain rich archacological information with concise expression. We establish the Vase baseline model through LoRA-baael (Hu al. 2022) supervised fine-
tuning (SFT) Ouyang et al. 2022
Reinforcement Learning (RL) We later employ the GRPO (Shao et al. 2024 reinforcement learning method for further optimization. Our VaseVQA-3D dataset is naturally suited for RLVR (Lambert al., 2024) training, as the ground truth captions contain complete information across six dimensions (Fabric, Technique, Shape, Dating, Decoration, and Attribution). During the GRPO rollout phase, the model generates captions containing this dimensional information, which are then compared and verified against the standard answers in the ground truth, analogous to mathemat- ical problem-solving processes where the model generates answers for comparison with standard solutions.
As shown in Figure our GRPO strategy employs Reinforcement Learning with Verifiable Rewards (RLVR) to compute the reward function. This approach decomposes archaeological descriptions into six core semantic dimensions for evaluation, each with corresponding weights: Fabric (w = 0.20), Technique (w, 0.20). Shape (w, 0.15), Dating (wa 0.15), Decoration (waee 0.20), and Autriuution (w, = 0.10). The dimensional reward r; is calculated as:
sim(g.;44).
if sim(gi, (,) otherwise
T1
0,
(1)

---

VaseVD Benchmarking 3D VLMs on Ancient Greek Pottery
Table 1: CLIP-based Data Quality Filtering Results.
Filtering Stage
Input Images
Output Images
Retention Rate
Quality Score
Initial Collection
30,000
30,000
100%
Resnet-50 Quality Filtering
30,000
13,599
45.3%
CLIP Fragment Filtering
13,599
6,330
46.5%
0.156
CLIP View Selection
6,330
3,880
61.3%
0.234
3D Generation (TripoSG)
3,880
664
17.1%
Overall Pipeline
30,000
664
2.2%
0.234
Table 2: 3D Generation Methods Comparison on 24 Ground Truth Models.
Method
PSNR1
SSIM
LPIPS.
CD1
NCT
CLIP-I1
CLIP-TT
Reference
15-25
0.7-0.9
0.1-0.3
0.1-0.3
0.6-0.8
0.7-0.9
0.6-0.8
TripoSG (Li ct al.
2025
17.21
0.8676
0.1308
0.1490
0.7232
0.8896
0.9594
Hunyuan3D Zhao al. 2025
17.23
0.8657
0.1319
0.1515
0.7389
0.8837
0.9237
where 9i and i; represent the generated content and target content for dimension i, sim(::) denotes cosine similarity, and 0.7 is the similarity threshold. addition to content accuracy assessment, our reward function includes quality control penalty mechanisms:
P 0o Plenghh + O Prepetition + Og irrelevant:
(2)
which penalizes inappropriate length, repetitive phrasing. and irrelevant content respectively, with penalty weights set to 0.1, 0r, 0.1, and a; 0.15. The final reward function is:
R m. T4 P P+B. B,
where B is sequence matching-based similarity reward term, and the complete reward is con- strained the unit interval [0, 1] ensure training stability during policy optimization.
Through this complete pipeline of data construction, model training, and evaluation, we success- fully established an end-to-end system from 2D images to 3D models to specialized VQA models, providing an effective technical solution for intelligent analysis of ancient Greek pottery.
5
EXPERIMENTS
This section presents comprehensive experimental evaluation of our pipeline for constructing Vase VQA-3D datasets and training specialized models for ancient Greek vase analysis. We con- duct extensive experiments validate the effectiveness of each component in methodology, compare against state-of-the-art baselines, and provide detailed analysis the results across mul- tiple evaluation dimensions. Our experiments focus on three key aspects: CLIP-based data quality filtering, 3D generation method comparison, and dataset quality assessment.
5.1 DATASETS AND EVALUATION METRICS
Experimental Datasets. This study three datasets for experimental validation at different stages. VaseVQA Original Dataset contains 30,000 ancient Greek vase images as the input data source for our pipeline. VaseVQA-3D the core dataset after quality filtering and 3D generation, containing 664 3D vase models and 4,460 question-answer pairs, divided into training set (420 models), validation set (90 models), and test set (90 models) with a 70%/15%/11 5% split. VaseE- val contains 24 professional GLB files collected from Sketchfab ground truth references for 3D generation method evaluation.
We adopt three-category evaluation metric system. 3D Generation Qual- ity Metrics are used for VaseEval validation, including visual quality metrics (PSNR (Wang et al. 2004). SSIM (Hore & Ziou, 2010), LPIPS (Johnson al., 2016)), geometric accuracy metrics (Chamfer Distance(CD) (Fan et al. 20176). Normal Consistency(NC) (Li al. 2023a)). and se- mantic consistency metrics (CLIP Image/Text Similarity) (Radford al. 2021
Evaluation Metrics.
VQA Capability
7

---

VaseVD Benchmarking 3D VLMs on Ancient Greek Pottery
Table Comprehensive Dataset Quality Assessment Results by Individual Models.
Method
FID4 CLIPT
R@ 107
R@st
R@1f
Lexical Sim.t
3D- SDecciaiad Models
DiffuRank (Lno et al. 2024
0.421
0.798
16.67%
8.33%
2.08%
0.274
Cap3D [uo al. 2023
0.445
0.792
14.58%
7.29%
1.56%
0.267
LLa Zhu et al. 2024
0.494
0.784
10.42%
5.21%
1.04%
0.238
Closed-source VLMs
Gemini-2.5-flash (Comanici et 2025
0.325
0.736
28.57 %
17.58%
2.20%
0.210
Claude-4-sonnet Anthropid 2025b
0,353
0.676
23.96%
10.42%
3.12%
0,188
Gemini-2,5-Pro. Comanici et 2025
0.397
0.680
22.92%
14.58%
3.12%
0.162
GPT-4.1 (OpenAT
2025
0,501
0.644
25.00%
10.42%
3.12%
0.128
Claude-3.5-sonnee Anthropic 2024
0.455
0.643
15.62%
8.33%
2.08%
0.116
Doubao- 1-5.viiioonnn-------
Banee 2025
0,504
0.606
14.58%
4.17%
1.04%
0.074
GPT-4o Hurst et
2024
0.582
13.54%
6,25%
2.08%
0.104
Claude sonnet Anthropic
2025a
0.600
0.339
13.54%
6,25%
1.04%
0.101
Open-sounce VL.My
InterVLL (Chen et al. 2024
0.376
0.771
10.42%
8.33%
2.08%
0.252
Qwen2.5-VLL-7B
VL-BB
Ba et al
2025
0,334
0.775
18.75%
9.38%
2.08%
0.217
Qwen2.5.VLs VL-3B
Bai et al.
2025
0,358
0.782
9.38%
6,25%
1.04%
0,259
VaseVL Ge et al 2025
0.493
0.790
10.4%
6.25%
2.08%
0,255
YuscVLM-3B-SFT (Ours)
0.359
0.788
17.71%
8,33%
2.08%
0,223
VaseVLM-3B-RI (Ours)
0,363
0.789
17.71%
10.42%
2.08%
0,245
VaseVLM-7B-SFFT (Ours)
0,332
0.779
20.83%
10.42%
3.12%
0,272
VaseVLM-7B-RL (Ours)
0.328
0.792
21.24%
11.12%
3.52%
0.276
Evaluation Metrics are used for model performance assessment, including FID Score (Heusel al. 2017). CLIP Score (Shen et al., 2021), retrieval metrics R@ 1/5/10 (Fang et al.) 2015), and lexi- cal similarity (Lin 2004). Human Evaluation Metrics employ 10 experts to model-generated captions on a 0-5 scale, evaluating description accuracy and cultural appropriateness.
5.2 IMPLEMENTATION DETAILS
Training Setup. All experiments conducted high- performance computing cluster equipped with NVIDIA A100 GPUs. The hardware configuration includes 8x NVIDIA A100 GPUs (80GB VRAM each), 2x Intel Xeon Platinum 8358 CPUs (32 cores each), 1TB DDR4 memory, and 10TB NVMe SSD storage. The total computational time for the entire experimental pipeline is approx- imately 14 days
single A100, including 13.5 days for 3D generation, 4 hours for supervised fine-tuning, and 20 hours for reinforcement learning training. For detailed hyperparameters and implementation settings, please refer to Section A.4
Experimental Workflow. We adopt a three-stage progressive mechanism to construct our Vase dataset and train specialized models. The first stage involves data filtering using ResNet-50 and CLIP-based quality assessment to select high-quality vase images from the original VQA dataset. The second stage focuses on generation and dataset construction, where we employ TripoSG for single-image 3D reconstruction and generate corresponding video sequences with enhanced captions. The third stage encompasses model training and evaluation, including su- pervised fine-tuning and reinforcement learning optimization of VaseVLM variants. For detailed technical implementation, please refer to Appendix A.5
5.3 EXPERIMENTAL RESULTS
Our comprehensive experimental evaluation demonstrates the effectiveness of each component in our pipeline for constructing high-quality VaseVQA-3D datasets and training specialized models for ancient Greek vase analysis. The results validate approach across multiple dimensions, including data quality filtering, 3D generation method selection, and dataset quality assessment.
Data Quality Filtering Effectiveness Analysis. Based the results in Table [ our three-stage progressive filtering strategy demonstrates high selectivity and effectiveness. Starting from 30,000 initial images, the classifier quality filtering retains 45.3% of images, effectively removing blurry, overly dark, and low-resolution samples. The subsequent CLIP fragment filtering further screens 46.5%, 5%, successfully identifying and removing vase fragments. The CLIP view selection stage
8

---

VaseVQA-3D: Benchmarking 3D VLMs on Ancient Greek Pottery
Table 4: Human Evaluation Results: Expert Ratings for Vase Caption Generation (Scale: 0-5).
Method
Exp-1
Exp-2
Exp-3
Exp-4
Exp-5
Exp-6
Exp-7
Exp-8
Exp-9
Exp-10
Ave.
Rank
Fine-tuned Models (Ours)
VaseVLM-7BB.
4.6
4.5
4.7
4,4
4.5
4.4
4.7
VascVLM--B-RLL
4.4
4.6
4,3
4,5
4.2
4,4
4,3
4.2
4,5
4.3
4.37
2
4.2
4.4
4.1
4,3
4.0
4.2
4.1
4.0
4.3
4.1
4.17
3
1BeeVLM-3B-SFF
4.0
4.2
3,9
4.1
3.8
3.9
3.8
4.1
3.97
JD-Specialized Models
DiffuRunk
4.1
4.3
4.0
4.2
3.9
4.1
4.0
3.9
4.2
4.0
4,07
5
Generul parpuse VLMs
Gemini-2.5-lash
3.9
4.1
3.8
4.0
3.7
3.9
3.8
3.7
4.0
3.87
VLseVL
3.8
4.0
3.7
3.9
3.6
3.8
3.7
3.6
3.9
3.7
3.77
7
Claude-4-sonnet
3.7
3.9
3.6
3.8
3.5
3.7
3.6
3.5
3.8
3.67
Qwen2.5-Vi..
3.6
3.8
3.5
3.7
3.4
3.6
3.5
3.4
3.7
3.5
3.57
9
Gemini-2.5-PP
3,5
3.7
3.4
3.6
3.3
3.5
3.4
3.3
3.6
3.4
3.47
10
Overall Average
4.0
4.2
3,9
4.1
3.8
4.0
3.9
3.8
4.1
3,97
has retention rate of 61.3%, ensuring that only the best viewing angle is retained for each vase, ultimately obtaining 3,880 high-quality images.
The quality score significantly improved from 0.156 after fragment filtering to 0.234 after view selection, an improvement of 50%. After the complete filtering pipeline, the TripoSG 3D generation process successfully produced 664 high-quality GL.B models from 3,880 images, with a generation success rate of 17.1% 1%
3D Generation Method Comparison Analysis. To select the optimal 3D generation method for dataset construction, we conducted comprehensive comparison between TripoSG and Hun- yuan3D using our VaseEval validation set. The evaluation encompasses seven key metrics including image quality, geometric accuracy, and semantic consistency. Table _ shows that while both meth- ods perform competitively. TripoSG demonstrates superior performance in geometric reconstruction accuracy and semantic understanding, making it more suitable for archaeological VQA applications. For detailed comparative analysis, please refer to Appendix A.6
Comprehensive Dataset Quality Assessment. After confirming the effectiveness of our 3D gen- eration approach, we proceed to generate our complete 3D vase dataset consisting of 664 models. To ensure the quality and reliability of our constructed dataset, as shown Table we establish comprehensive evaluation framework using multiple state-of-the-art VLMs baselines. We eval- uate both the QA effectiveness and caption quality of our generated dataset across different model categories, including -D-specialized models. advanced 3D VLMs, and general pureuose VLMs. For detailed experimental analysis, please refer to Appendix A.7 For detailed examples across VLMs, please refer Appendix A.9 and A.8
Our Vase VLM models perform excellently across all metrics, validating the effectiveness of special- ized training. VL--VLL achieves the best comprehensive performance (FID: 0.328, CLIP: 0.792, R@ @ 10: 21.24%), maintaining low FID while achieving high CLIP scores and retrieval accu- racy.
Performance Comparison and Baseline Selection. Our Vase VLM-7B-RL demonstrates superior performance across multiple metrics. Specifically, we achieve 3.52% in R@ accuracy, representing a 12.8% relative improvement over Claude-4-snnett's 3.12% (the highest R@ among all baseline models). lexical similarity, our model achieves 0.276, 6.6% improvement over Qwen2.5-Vi 3B's 0.259 (the best performance in this metric among comparable models). We compare against different baselines for different metrics because no single baseline model excels across all evaluation dimensions, reflecting the multi-faceted nature of archaeological VQA tasks.
important note that 3D-specialized models (DiffuRank, Cap3D, LLLVAAD) operate under fundamentally different task settings: they generate descriptions directly from GLB files with com- plete 3D geometric information, while our Vase LLM understands 3D content from 2D rotation videos, requiring spatial reasoning and 3D structure inference. These models included for com- pleteness but are not directly comparable due to different input modalities.
Human Evaluation Results Analysis. The expert evaluation results in Table 4 further validate the superiority of our approach. VaseVLM-7B-RR achieved the highest average score of 4.57, receiv- ing consistent recognition from 10 archaeological experts. Reinforcement learning trained mod-

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
els (VaseVLM-7B-RRL. 4.57, VLM- 4.37) significantly outperformed supervised fine- tuning versions (VaseVLM-77) VLM-7B- 4.17, VLseVLM-3B-SBBB 3.97), with an average improvement of approximately 0.4 points, demonstrating the effectiveness of the GRPO method in improving description quality and cultural appropriateness.
Compared to baseline models, our best model surpassed all 3D-specialized models and general- purpose VLMs. Particularly compared to the best-performing baseline DiffuRank (4.07 points), VL--VLL achieved 12.3% improvement, demonstrating the significant effects of spe- cialized training and reinforcement learning optimization. These results validate our approach of domain-specific training and reinforcement learning optimization. While different baseline mod- els excel in different metrics, our VLM--B--BBBR achieves competitive or superior performance across all evaluation dimensions, demonstrating the effectiveness of specialized training for cultural heritage VQA tasks.
LIMITATIONS AND FUTURE WORK
While our Vase dataset and pipeline demonstrate effectiveness in 3D cultural heritage anal- ysis. several limitations exist. The 17.1% data retention rate from filtering reflects the quality chal- lenges in existing cultural heritage datasets. Our approach requires substantial computational re- sources for 3D) generation and model training.
However, we emphasize that our methodology is not limited to ancient Greek pottery. To validate the generalizability of our pipeline across different cultural heritage domains, conducted supple- mentary experiments on Chinese bronze artifacts and ancient Greek sculptures (see Appendix [A. 10), These experiments demonstrate that our end-to-end pipeline be effectively adapted other arti- fact types by customizing the domain-specinn reward dimensions, providing strong evidence for the broader applicability of our approach.
Future work should focus on: (1) improving 3D generation success rates through advanced recon- struction techniques; (2) extending the framework to other cultural heritage domains beyond ancient Greek pottery; (3) developing more efficient training methods to reduce computational requirements while maintaining quality.
7 CONCLUSION
This paper introduces Vase VQse the first 3D visual question answering dataset for ancient Greek pottery analysis, along with a complete end-to-end pipeline for cultural heritage Al applications. We construct a high-quality dataset containing 664 3D vase models with 4,460 question-answer pairs through systematic filtering and generation processes. Our VaseEval validation set enables reliable 3D generation method selection and quality assessment. Our specialized VLM models achieve significant improvements over existing approaches: Vase achieves 12.8% improvement in R@1 accuracy and 6.6% improvement in lexical similarity compared to the strongest baselines in their respective metrics. The models demonstrate superior performance in archaeological termi- nology understanding, validating the effectiveness of domain-speciife training for cultural heritage applications.
Importantly, our methodology not limited to ancient Greek pottery. Supplementary experiments on Chinese bronze artifacts and ancient Greek sculptures (Appendix I0. demonstrate that our pipeline can be effectively generalized to other cultural heritage domains by customizing domain- specific reward dimensions. This generalizability validates our approach as a scalable framework for digital heritage preservation across diverse artifact types.
Vase establishes new benchmark for Al-driven cultural heritage analysis, providing both dataset and methodology for future research. This work demonstrates the potential of specialized AI systems in preserving and understanding cultural heritage, contributing to interdisciplinary collabo- ration between computer science and archaeology, and offering a replicable framework for extending Al applications to other cultural heritage domains worldwide.
Acknowledgements. This work is supported by the Fundamental Research Funds for the Central Universities, Peking University.
10

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
REFERENCES
Efstathios Adamopoulos, Fulvio Rinaudo, and Liliana Ardissono. A critical comparison of 3d digi- tization techniques for heritage objects. ISPRS International Journal of Geo- Geo-Information, 10(1):
10, 2020.
Anthropic.
Claude 3.5 sonnet.
httpss//ww..anttropc....cco./s
claude 3- 5- sonnet
2024. Accessed: 2025-09-17.
Anthropic.
Claude 3.7 sonnet and claude code. httpss//ww.annthropc...coo//// 2025a. Accessed: 2025-09-17.
claude-3-
Anthropic. Introducing claude 4.
https:/ bttpss:/www..r http://wwww..tttopccc.ccoo http://ppw....ccoe////////aaaeee 2025b. Accessed: 2025-09-17. Official technical announcement for Claude Sonnet 4.
Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, and Motoaki Kawanabe. Scanqa: 3d question an- swering for spatial scene understanding. In 2022 IEEE/CVF Conference on Computer Vision and Pottern Recognition (CVPR), pp. 19107-19117, 2022. doi: 10.1 1011000RR2268...... 2022.0182
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang. Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502 13923, 2025.
ByteDance. doubao-1.55 1.5-vision-pro.
1553586 2025. Accessed: 2025-09-17 (In Chinese).
Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexander W. Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. Generative novel view synthesis with 3d-aware diffusion models. In JEEE/CVE ICCV, pp. 4194-4206, 2023.
Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing. Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual- visual-linguistic tasks. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 24188-24198, 2024.
Casey Chu, Andrey Zhmoginov, and Mark Sandler. Cyclegan, master of steganography. arXiv preprint arXiv:1712.02990 2017.
Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capa- bilities. arXiv preprint arXiv:2507.06261 2025.
Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, Jae Sung Park, Moham- madreza Salehi, Niklas Muennighoff, Kyle Lo, Luca Soldaini, Jiasen Lu. Taira Anderson. Erin Bransom, Kiana Ehsani, Huong Ngo, YenSung Chen, Ajay Patel, Mark Yatskar, Chris Callison- Burch, Andrew Head, Rose Hendrix, Favyen Bastani, Eli VanderBilt, Nathan Lambert, Yvonne Chou, Arnai Chheda, Jenna Sparks, Sam Skjonsberg, Michael Schmitz, Aaron Sarnat, Byron Bischoff, Pete Walsh, Chris Newell, Piper Wolters, Tanmay Gupta, Kuo-Hao Zeng, Jon Bor- chardt, Dirk Groeneveld, Crystal Nam, Sophic Lebrecht, Caitlin Wittlif, Carissa Schocnick, Oscar Michel, Ranjay Krishna, Luca Weihs, Noah A. Smith, Hannaneh Hajishirzi, Ross Girshick, Ali Farhadi, and Aniruddha Kembhavi. Molmo and pixmo: Open weights and open data for state-of- the-art vision-language models. Proceedings of the IEEÉ/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 91-104, June 2025.
Jiajun Deng, Tianyu He, Li Jiang, Tianyu Wang. Feras Dayoub, and Ian Reid. 3d-llava: Towards generalist Imms with omni superpoint transformer. Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 3772-3782, 2025.
Haoqiang Fan, Hao Su, and Leonidas J. Guibas. A point set generation network for 3d object reconstruction from single image. In IEEF/CVF CVPR, pp. 2463-2471, 2017a.
11

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object recon- struction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 905-613, 2017b.
Hao Fang, Saurabh Gupta, Forrest landola, Rupesh K Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C Platt, et al. From captions to visual concepts and back. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp- 1473-1482, 2015.
Haixia Feng, Qingwu Hu, Pengcheng Zhao, Daoyuan Zheng, Mingyao Ai, Siliang Chen, and Xiyu Hu. Automatic generation of chinese mural line drawings via enhanced edge detection and cyclegan-based denoising, npj Heritage Science, 13(1):345, 2025.
Jinchao Ge, Tengfei Cheng, Biao Wu, Zeyu Zhang, Shiya Huang, Judith Bishop. Gillian Shepherd, Meng Fang, Ling Chen, and Yang Zhao. Vasevqa: Multimodal agent and benchmark for ancient greek pottery. arXiv preprint vrXiv:2509.17191 2025.
Rohit Girdhar, David F. Fouhey, Mikel Rodriguez, and Abhinav Gupta. Learning a predictable and generative vector representation for objects. In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (eds.), ECCV, volume 9910, pp. 484-499, 2016.
Yash Goyal, Tejas Khot, Douglas Summers-Stay Dhruv Batra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6904-6913, 2017.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Fangzhou Hong, Jiaxiang Tang, Ziang Cao, Min Shi, Tong Wu, Zhaoxi Chen, Shuai Yang. Tengfei Wang. Liang Pan, Dahua Lin, et al. 3dtopia: Large text-to-3d generation model with hybrid diffusion priors. arXiv preprint arXiv:2403.0m 022334, 2024.
Alain Hore and Djemel Ziou. Image quality metrics: Psnr vs. ssim. In 2010 20th international conference on pattern recognition, pp. 2366-2369. IEEE, 2010.
Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang,
Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022.
Ting Huang, Zeyu Zhang, and Hao Tang. 3d-r1: Enhancing reasoning in 3d vims for unified scene understanding. arXiv preprint
2025a.
Ting Huang, Zeyu Zhang, Yemin Wang, and Hao Tang, 3d coca: Contrastive learners are 3d cap- tioners. arXiv preprint arXiv:2504.099 09518, 2025b.
Ting Huang, Zeyu Zhang, Ruicheng Zhang, and Yang Zhao. De-scene: Data-centric learning for 3d scene understanding. arXiv preprint arXiv:2505. -=Xiv:2505./52222 /5232, 2025c.
Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Go system card. arXiv preprint arXiv:2410.21276. 2024.
Pablo Jaramillo and Ivan Sipiran. Cultural heritage 3d reconstruction with diffusion networks. In European Conference on Computer Vision, pp. 104-117. Springer, 2024.
Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In European conference on computer vision, pp. 694-711. Springer, 2016.
Rahima Khanam and Muhammad Hussain. What is yolov5: A deep look into the internal features of the popular object detector. arXiv preprint arXiv:2407.20892, 2024.
Zeqiang Lai, Yunfei Zhao, Haolin Liu, Zibo Zhao, Qingxiang Lin, Huiwen Shi, Xianghui Yang, Mingxin Yang, Shuhui Yang, Yifei Feng, et al. Hunyuan3d 2.5: Towards high-fidelity 3d assets generation with ultimate details. arXiv preprint arXiv:2506. 16504, 2025.
12

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brah- man, Lester James V Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, et al. Tulu 3: Pushing frontiers in open language model post-training, arXiv preprint arXhv:2411.15124 2024.
Yushi Lan, Fangzhou Hong, Shuai Yang, Shangchen Zhou, Xuyi Meng. Bo Dai, Xingang Pan, and Chen Change Loy. Lnšdiff: Scalable latent neural fields diffusion for speedy 3d generation. In European Conference on Computer Vision, pp. 112-130. Springer, 2024.
Jingliang Li, Zhengda Lu, Yiqun Wang, Jun Xiao, and Ying Wang. Nr-mvsnet: Learning multi- view stereo based on normal consistency and depth refinement. IEEE Transactions on Image Processing, 32:2649-2660 2023a.
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-imag pre- training for unified vision-language understanding and generation. In International conference on machine learning. pp. 12888- 12900. PMLR, 2022.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning, pp. 19730-19742. PMLR, 2023b.
Weiyu Li, Jiarui Liu, Hongyu Yan, Rui Chen, Yixun Liang, Xuelin Chen, Ping Tan, and Xiaoxiao Long. Craftsman3d: High-fidelity mesh generation with 3d native generation and interactive geometry refiner. arXiv preprint arXiv:2405. I4V,79995.4499, 2024.
Yangguang Li, Zi-Xin Zou, Zexiang Liu, Dehu Wang. Yuan Liang, Zhipeng Yu, Xingchao Liu, Yuan-Chen Guo, Ding Liang, Wanli Ouyang, et al. Triposg: High-fidelity 3d shape synthesis using large-scale rectified flow models. arXiv preprint arXiv:2502.0666/ 2025.
Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen. Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching. In JEEE/CVF CVPR, pp- 6517-6526, 2024.
Chen-Hsuan Lin, Jun Gao, Luming Tang. Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming- Yu Liu, and Tsung- Yi Lin. Magic3d: High-resolution text-to-3d content creation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 300-309, 2023.
Chin-Yew Lin. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pp. 74 81, 2004.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lec. Visual instruction tuning, Advances in neural information processing systems, 36:34892-34916, 2023a.
Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su. One- 2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization. Advances in Neural Information Processing Systems, 14422226--2246,, 2023b.
Qingxiang Liu, Ting Huang, Zeyu Zhang, and Hao Tang. Nav-rl: Reasoning and navigation in embodied scenes. arXiv preprint arXiv:2509. 2025.
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3; Zero-shot one image to 3d object. In /EEE/CVF ICCV, pp. 9264 9275, 2023c.
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference computer vision, pp. 10012-10022, 2021.
Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Mare Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 9970-9980, 2024.
Tiange Luo. Chris Rockwell, Honglak Lee, and Justin Johnson. Scalable 3d captioning with pre- trained models. Advances in Neural Information Processing Systems, 36:75307-75377, 2023.
13

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
Tiange Luo, Justin Johnson, and Honglak Lee. View selection for 3d captioning via diffusion rank- ing. In European Conference on Computer Vision, pp. 180-197. Springer, 2024.
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Oc- cupancy networks: Learning 3d reconstruction in function space. In Proceedings the IEEE/CVF conference on computer vision and pattern recognition, pp.
2019.
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1)999-106, 2021.
OpenAI. Introducing gpt-4.1 in the api. Accessed: 2025-09-17, 17; Official technical announcement for GPT441
http:p/awaa....oooooooo//gggeaea 2025. GPT441 mini, and GPT-4.1
nano.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to fol- low instructions with human feedback. Advances neural information processing systems, 35: 27730-27744, 2022.
Jiao Pan, Liang Li. Hiroshi Yamaguchi. Kyoko Hasegawa, Fadjar Ibnu Thufail, Brahmantara, Xiao- juan Ban, and Satoshi Tanaka. Reconstructing, understanding, and analyzing relief type cultural heritage from single old photo. In Proceedings of the 32nd ACM International Conference Multimedia, pp. 7724-7733, 2024.
Yohann Perron, Vladyslav Sydorov, Adam P Wijker, Damian Evans, Christophe Pottier, and Loic Landrieu. Archaeoscape: Bringing aerial laser scanning archaeology the deep learning era. Advances in Neural Information Processing Systems, 37:25888-25911 2024.
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988. 2022.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. 8748-8763. PmLR, 2021.
Olaf Ronneherger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedi- cal image segmentation. In International Conference Medical image computing and computer- assisted intervention, pp. 234-241. Springer. 2015.
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song. Xiao Bi. Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathemati- cal reasoning in open language models. arXiv preprint arXiv:2402.03300 2024.
Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai- Wei Chang, Zhewei Yao, and Kurt Keutzer. How much can clip benefit vision-and- lnd-uag tasks? arXiv preprint arXiv:2107.063883 2021.
Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zerol23++: a single image to consistent multi-view diffusion base model. CoRR, abs/2310.1511 2023.
Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Fu Yujie, Zeyu Zhang, Shiyu Jiang, Miao Fang, et al. Hazards in daily life? enabling robots to proactively detect and resolve anomalies. In Proceedings of the 2025 Conference of the Nations of the Amer- icas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume Long Papers), pp. 7399-7415, 2025a.
Zirui Song, Guangxian Ouyang, Mingzhe Li, Yuheng Ji, Chenxi Wang. Zixiang Xu, Zeyu Zhang, Xiaoqing Zhang, Qian Jiang, Zhenhao Chen, et al. Maniplvm-rl: Reinforcement learning for reasoning in embodied manipulation with large langunelanguage models. arXiv preprint arXiv:2505. 16517, 2025b.
14

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arXiv:2309.1663 2023a.
Junshu Tang. Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen. Make- it-3d: High-fidelity 3d creation from single image with diffusion prior. IEEE ICCV, pp- 22762-22772, 2023b.
Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, and Greg Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In IEEE CVPR, pp. 12619- 12629, 2023a.
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang. Pixel2mesh: Generating 3d mesh models from single rgb images. In Proceedings of the European conference on computer vision (ECCV), pp. 62-67,20 2018.
Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolific- dreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. In NeurlPS, 2023b.
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero Simoncelli. Image quality assessment: from error visibility to structural similarity. JEEE transactions on image processing, 13():600 612, 2004.
Markus Worchel, Rodrigo Diaz, Weiwen Hu, Oliver Schreer, Ingo Feldmann, and Peter Eisert. Multi-view mesh reconstruction with neural deferred shading. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 6187-6197, 2022.
Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, and Josh Tenenbaum. Marmet:
3d shape reconstruction via 2.5d sketches. In NeurlPS, pp. 540-550, 2017.
Rundi Wu, Yixin Zhuang, Kai Xu, Hao Zhang, and Baoquan Chen. PQ-NET: A generative part seq2seq network for 3d shapes. In /EEE/CVF CVPR, pp. 826 826-835, 2020.
Shuang Wu, Youtian Lin, Feihu Zhang, Yifei Zeng, Jingxi Xu, Philip Torr, Xun Cao, and Yao Yao. Direct3d: Scalable image-to-3d generation via 3d latent diffusion transformer. Advances in Neural Information Processing Systems, 37:121859-121888 2024.
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann. Disn: Deep implicit surface network for high-quality single-view 3d reconstruction. Advances in neural in- formation processing systems, 32, 2019.
Hong Yang. Shaohua Wang, Shunli Wang. Pengcheng Zhao, Mingyao Ai, and Qingwu Hu. Moated site object detection using time series satellite imagery and an improved deep learning model in northeast thailand. Journal of Archaeological Science, 171:106070, 2024.
Angen Ye. Zeyu Zhang, Boyuan Wang, Xiaofeng Wang, Dapeng Zhang, and Zheng Zhu. Vla-rl: 2025.
Enhancing reasoning in vision-language-action models. arXiv preprint
Taoran Yi, Jiemin Fang, Junjie Wang. Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. In IEEE CVPR, pp. 6796-6807, 2024.
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 4578 2021.
Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Angi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions on Graphics (TOG), 43(4):1-20, 2024.
Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, et al. Hunyuan3d 2.0: Scaling diffusion models for high resolution textured 3d assets generation. arXiv preprint arXiv:2501. 12202, 2025.
15

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
Chenming Zhu, Tai Wang, Wenwei Zhang, Jiangmiao Pang, and Xihui Liu. Llava-3d: A simple yet effective pathway to empowering Imms with 3d-awareness. arXiv preprint arXiv:2409.18142 2024.
Zixin Zou, Weihao Cheng, Yan-Pei Cao. Shi-Sheng Huang, Ying Shan, and Song-Hai Zhang. Sparse3d: Distilling multiview-consisten diffusion for object reconstruction from sparse views. In AAAI, pp. 7900-7908, 2024.
16

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
A APPENDIX
A.1 LLM USE DECLARATION
Large Language Models (ChatGPT) were used exclusively to improve the clarity and fluency of English writing. They were not involved research ideation, experimental design, data analysis, or interpretation. The authors take full responsibility for all content.
A.2 DETAILED RELATED WORK
Vision- Language Models and Visual Question Answering Vision-Language Models (VLMs) serve as core technology in multimodal AI, enabling machines to understand and reason about visual content through natural language. Modern VLM development grounded in contrastive learning, with CLIP (Radford et al.) 2021) having pioneered large-scale visual-text alignment. Building on this foundation, BLIP (Li et al. (022) and BLIP-2 (Li et al. 2023b) established unified frameworks for vision- vision-language understanding and generation, while LLaVA (Liu et al. 2023a) achieved break- throughs in multimodal tasks by integrating vision encoders with large language models.
Recent large VLMs have significantly enhanced visual understanding capabilities. Closed-source models like GPT-4V (Hurst et al.] 2024) and Gemini (Comanici et al.] 20255) excel at complex visual reasoning, while open-source models such Qwen2.5-VL. (Bai al. (2225) and InternVL, (Chen
et al. 2024) provide powerful multimodal tools for the research community.
Specialized techniques have emerged in 3D vision- langun- understanding (Huang al.) 2025a c
Liu et al. 2025 Song et al., 2025a b) Ye et al. 2025): Cap3D (Luo et al.) (023) advanced 3D-
text data construction through large-scale 3D object descriptions, DiffuRank (Luo al. 2024 im-
proved caption generation accuracy via optimized rendered view selection, and LLaVA (Zhu
al. 2024) with 3D-LLa VA (Deng et al. 2025) achieved high-quality 3D model descriptions and
question-answering
Traditional 2D visual question answering datasets like VQAv2 (Goyal al. 2017 have driven vi-
sual reasoning development but cannot handle 3D spatial complexity. Early 3D VQA works such as ScanQA (Azuma et al.] 2022) focused on indoor spatial relationships, establishing foundations for 3D question answering. However, existing methods show significant limitations when processing cultural artifacts with complex geometrie structures and cultural significance. 3D VQA applications in cultural heritage remain underexplored, particularly for ancient Greek pottery with intricate deco- rative patterns and profound historical meaning. This work constructs the first ancient Greek pottery VQA-3D dataset address this gap.
3D Generation and Reconstruction image-to-3DD model generation, DreamFusion (Poole et al,, 2022) pioneered the application of image diffusion priors to 3D generation, proposing the Score Distillation Sampling (SDS) method, which enabled iterative optimization of 3D representations via differentiable volume rendering (Mildenhall et al., (002T). Subsequent studies have made substantial
improvements in multiple directions, including 3D representation forms (Lin et al.) 2023 Tang
al.
2023a Yi et al., 2024). sampling strategies (Liang et al., 2024 Wang al. 2023a Zou
et al. 2024). integration of additional geometric cues (Long et al. 2024 Tang et al. (023b). and
consistency multi-view image generation.
Furthermore, numerous studies have explored training viewpoint-aware image diffusion models based on input images (Chan al. 2023) Liu et al., 2023c Shi al.) 2023). range of research has proposed learning geometric structures in various representation forms from input images through an encoder-decoder network architecture within deterministic process-such such as point clouds (Fan) ct al. 2017a; Wu et al., 2020), voxels (Girdhar et al., 2016)) Wu et al.) 2017). meshes (Wang et al. Worchel et al. 2022), or implicit fields (Mescheder et al. 2019 Xu et al.) 2019 Yu et al,
2018
2021
One-2-3-45 (Luu et al. 2023b) was the first to propose combining a 2D image diffusion model
with a multi-view reconstruction model, achieving generative capabilities while maintaining fast reconstruction speed. Recently, some researchers have attempted to train latent 3D diffusion models
based massive high-quality 3D models (Hong et al. 2024 Lan al. 2024
Li et al. 2024
Wu
17

---

VaseVQA-3D: Benchmarking 3D VLMs on Ancient Greek Pottery
et al. 2024 Zhang al. 2024), demonstrating impressive 3D generation results. However, these methods still have limitations in the task of 'hihhfidelitty generation with image alignment."
TripoSG (Li et al.] 2025) adopted a 3D representation with stronger geometrie expression abil- ity, improved the diftusion model architecture and training strategics, and achieved state-of-the-an performance in 3D shape generation tasks. Hunyuan3D (Lai al. 2025) employed a two-stage approach: first, it used a multi-view diffusion model to generate view RGB images, and then converted these images into 3D assets using Transformer-bast large-scale reconstruction model for sparse viewpoints.
Cultural Heritage and Archacological AT Currently, Al technologies are effectively enhancing the level of cultural heritage preservation. ArchaeoScape (Perron et al. (022)) constructed the world's largest archaeological LiDAR dataset, successfully identifying ancient architectural remains under jungle cover through semantic segmentation models (such as U-Net (Ronneberger et al. (015) and Swin Transformer (Liu et al. 2021)), with accuracy significantly superior traditional manual inter- pretation. (Yang et al.) 2024) proposed an improved YOLOv5s model (Khanam & Hussain 2024), combining multispectral data with vegetation indices, identifying 116 moat sites in northeastern Thailand with 100% detection accuracy, representing vivid example of identification technology and cultural heritage protection. (Feng et al. (025) proposed an automated mural line drawing gen- eration method combining CLAHE edge enhancement, neural network (MLineNet), and CycleGAN CCu et al. 2017) denoising. Using Dunhuang murals as test subjects, the restoration results outper- formed existing algorithms in detail, clarity, and smoothness metrics, achieving Q-value of 89.26% These studies systematically demonstrated the technological breakthroughs and ethical challenges of Al in cultural heritage preservation and archaeological research, providing important references for interdisciplinary collaboration. In the field of cultural heritage 3D reconstruction, Adamopoulos
al. (2020) critically compared 3D digitization techniques to clarify their application boundaries, Jaramillo & Sipiran (2024) proposed method using diffusion networks to address incomplete data issues, and |an et al. (2024) realized 3D reconstruction of relief heritage from single old photos, collectively advancing related research.
A.3 DATASET FEATURES AND COMPOSITION
Our Vase VQs-VQAA3D dataset constitutes the first comprehensive evaluation resource specifically de- signed for 3D ancient Greek pottery visual question answering, filling the gap in existing VQA datasets in the cultural heritage domain. Table 5 shows detailed statistical comparisons from the original Vase dataset to our final VaseVQA-3D dataset. The strict filtering process reduced the dataset from 3,880 vase entries to 664 unique vase entries. Although the data retention rate is only 17.1%, this ensures that the final dataset achieves good standards in archaeological accuracy, image quality, and metadata completeness.
Regarding question type distribution, our dataset maintains a similar balance to the original Va- seVQA dataset, with core attribute questions (fabric, technique, shape, overall, dating, decoration) comprising the main proportion, while specialized attribute questions (attribution, provenance) have relatively lower coverage due to the incompleteness of archaeological records. Figure shows typ- ical examples from our VQA-3D dataset, including high-quality 3D vase models, structured question- answer pairs, and cleaned descriptive captions, demonstrating the dataset's comprehensive capabilities in supporting multimodal understanding of ancient Greek pottery.
GPT-4o Usage for Data Cleaning The archaeological metadata in the original Vase dataset already possesses strong structural information but contains significant noise in the complete de- scriptions, making them difficult use directly for model training. We use GPT-4o clean this noise and reorganize the metadata into coherent descriptions consistent with standard museum doc- umentation practices.
For example, the original fragmented format:
"The overall information The Vase Number is 14292; The Fabric is ATHENIAN:
The Technique is BLACK-FIGURE The Shape Name is LEKYTHOS; The Provenance is GREECE, ATTICA. MARATHON; The Date is -525 to 475; The Decoration is Body: HERAKLES AND THE BOAR (?); The Collection Record is Athens, National Museum: 1021;
18

---

VaseVD Benchmarking 3D VLMs on Ancient Greek Pottery
reorganized into:
"Athenian black-figure lekythos, 525-475 BCE, depicting Herakles and the boar; Marathon, Attica."
This cleaning process consolidates existing archaeological information without introducing new content. Importantly, our evaluation metrics are independent of language style: retrieval accuracy metrics (R @ I/R @ 5/R @ 10) measure semantic matching independent of stylistic variation; CLIP and FIID scores are computed from visual-semantic alignment rather than language patterns; and human evaluation by archaeological experts assesses archaeological accuracy rather than language style. Therefore, the evaluation results reward models that understand archaeological content, not those that merely imitate GPT-44'ss o language patterns.
Table 5: Dataset Statistics Comparison: Before and After Quality Filtering.
Metric
Original Vas
Filtered VaseVQA-31
Total Vase Entries
3,880
Total QA Pairs
26,101
4,460
Avg. QA per Entry
6,73
6.72
Fabric Questions
3,880 (14.9%)
664 (14.9%)
Technique Questions
3,880 (14.9%)
664 (14.9%)
Shape Questions
3,880 (14.9%)
664 (14.9%)
Caption Questions
3,880 (14.9%)
(14.9%)
Dating Questions
3,872 (14.8%)
664 (14.9%)
Decoration Questions
3,870 (14.8%)
663 (14.9%)
Attribution Questions
1,696 (6.5%)
280 (6.3%)
Provenance Questions
1,143 (4.4%)
197 (4.4%)
Question Types
Unique Images
3,880
Data Retention Rate
100%
17.1 1%
Quality Score
Mixed
High
A.4 HYPERPARAMETERS AND IMPLEMENTATION DETAILS
Data Filtering Stage ResNer-50 binary classifier: cross-entropy loss, Adam optimizer (learning rate le-4), 20 epochs, 512x512 pixel RGB input, confidence threshold 0.5. CLIP ViT-B/32 model: fragment removal threshold 0.1, text prompt "a complete intact vase viewed from the front".
3D Generation Stage TripoSG: 512x512 pixel front-view input, image preprocessing (normal- ization, denoising), approximately 5 minutes per sample (single A100), GLB format output. Blender 3.6: 16-frame 360-degree rotation videos, 512x512 pixels per frame, 2fps.
Post Training Stage Supervised Fine-tuming (SFT): Qwen2.5VL-33777 base models, LoRA rank 8, alpha 32, learning rate lc-4, batch size (16-step gradient accumulation). epochs, frozen ViT parameters, approximately 2 hours training time (single A100).
Reinforcement Learning (GRPO); policy gradient methods, policy update every 100 samples, learn- ing rate 1e-5, batch size 8, 10 epochs, approximately 10 hours training time (single A100).
Evaluation Metrics Quantitative: FID Score, CLIP Score, @ 1/5/10, lexical similarity. Qualita- tive: description accuracy, cultural appropriateness (10 archacological experts).
A.5 DETAILED WORKFLOW AND IMPLEMENTATION
We adopt three-stage progressive filtering mechanism process the original Vase dataset. The initial stage uses a ResNet-50 architecture binary classifier for coarse coarse-grained screening, auto- matically identifying and removing low low-quality images. Building this, introduce the CLIP ViT-B/32 model for fine-gree ane-grained semantic filtering, including fragment removal and optimal view selection.
19

---

VaseVD Benchmarking 3D VLMs on Ancient Greek Pottery
Table 6: Expert and Von-Expert Evaluation of TripoSG vs Hunyuan3D (Scale: 0-5).
Dimension
Method
Exp-1
Exp-2
Exp-3
Exp-4
Exp-5
Non-1
Non-2
Non-3
Non-4
Non-5
Ave.
TripoSG
4.5
4.5
4,4
4.35
Geometric Accurccy
Hunyuan3D
4.3
4.2
4.0
4.4
4,3
4,0
4.3
4.22
Decoration Fidelity
TripoSG
4.2
4.3
4.1
42
4.1
4.2
4.0
4.11
Hunyuan3D
4.3
4,4
4.1
4.2
4.1
4.2
4:24
Archacological Credibility
TripoSG
4.4
4.5
4.3
4.4
4.2
4.3
4.1
4.2
4.25
Hunyuan3D
Overall Average
Hunyuan3()
TripoSG
4.23
4.37
4.47
4.10
4.27
4.37
3,97
4,27
4,20
3.97
4,30
3.97
4,20
4.10
4.20
4.16
4.24
Subsequently, we conduct a systematic comparison between TripoSG and Hunyuan3D based on the VaseEval dataset. After determining TripoSG's advantages in generation quality, we adopt this method for single-image 3D reconstruction of filtered high-quality images. We then convert the generated GLB files video sequence format using Blender 3.6 for model training.
The supervised fine-tuning stage uses Qwen2.5VL. as base models, adopting LoRA for parameter- efficient fine-tuning. Building on SFT, we adopt the GRPO method for verifiable reinforcement learning training. Finally, we conduct comprehensive evaluation of the four trained Vase vari- ants using both quantitative metrics and human evaluation by archaeological experts.
A.6 DETAILED 3D GENERATION METHOD COMPARISON
Tableeppresents a comprehensive comparison of TripoSG and Hunyuan3D across seven dimensions on Vaselival. In terms of traditional image quality metries, both methods perform closely, but each has advantages. Hunyuan3D has a slight advantage in PSNR (17.23 vs 17.21), while TripoSG performs better in LPIPS (0.1308 vs 0.1319). In SSIM metrics, both perform comparably (TripoSG: 0.8676 vs Hunyuan3D: 0.8657).
In terms of geometric accuracy, the results show differentiation characteristics. TripoSG performs better in Chamfer distance (0.1490 vs 0.1515), showing its advantage in overall geometric recon- struction accuracy. However, Hunyuan3D performs better in normal consistency (0.7389 0.7232), indicating its capability in surface details and lighting interaction.
In semantic consistency evaluation, TripoSG demonstrates significant advantages. In CLIP image similarity, TripoSG (0.8896) slightly outperforms Hunyuan3I (0.8837). More importantly, in CLIP text similarity, TripoSG performs excellently (0.9594 vs 0.9237), exceeding Hunyuan3D by about 3.9%. Although our input is only single image rather than text descriptions, the CLIP text similarity metric reflects the matching degree between generated 3D models and predefined archaeological description templates, which is crucial for subsequent text generation tasks.
Comprehensive analysis shows that Hunyuan3D has slight advantages in traditional image quality metrics, while TripoSG performs better in geometric reconstruction accuracy and semantic consis- tency. Considering that our application scenario requires accurate 3D geometric structures and good semantic understanding capabilities to support archaeological description generation, TripoSG's 's ad- vantages in key metrics make it more suitable for our VQA task requirements.
To further validate the TripoSG selection, we conducted additional blind evaluation with 5 archaeol- ogists and 5 domain-unner unnelaedd individuals, assessing both methods on the VaseEval set across three dimensions: geometric accuracy, decoration fidelity, and archaeological credibility.
The human evaluation results confirm TripoSG's superiority, particularly in archacological credi- bility (4.25 4.02), which critical for cultural heritage applications. TripoSG achieves higher overall average (4.24 vs 4. 4.16), validating our method selection.
A.7 DETAILED DATASET QUALITY ANALYSIS
As shown in Table 3 Among 3D 3D-specialized models, DiffuRank performs best (FID; 0.421, CLIP: 0.798), with this advantage stemming from its specialized architecture design and training strategy for 3D scene understanding. DiffuRank adopts diffusion model framework that can better capture the complexity and spatial relationships of 3D geometric structures, which is particularly important when processing the three-dimensional forms of ancient Greek vases. Cap3D follows closely (FID:
20

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
0.445, CLIP: 0.792), with its advantages based on large-scale 3D-text pair training reflected in mantic understanding, but slightly inferior to DiffuRank in fine-grained control of generation quality. Although LLaVA3D performs relatively weakly in retrieval tasks (R @ 10: 10.42%) its multimodal fusion mechanism provides important references for subsequent model design.
General-purposs VLMs show significant performance differentiation characteristics. Gemini-2.5- flash performs excellently in retrieval tasks (R @ 10: 28.57%), benefiting from Google's pretraining advantages on large-scale multimodal data and its advanced attention mechanism design, enabling the model to better establish correspondences between visual features and text descriptions. How- ever, its relative disadvantage lexical similarity (0.210) reflects the limitations of general models in understanding professional archaeological terminology. This phenomenon is also reflected in other general models, such as GPT-4o achieving only 0.104 in lexical similarity, indicating that while large-scale general pretraining improves overall understanding capabilities, there are still de- ficiencies in mastering specific domain terminology.
Claude series models show progressive performance characteristics across versions, with Claude- 4-sonnet (FID; 0.353) significantly outperforming Claude-3.5 (FID: 0.455) and Claude-3.7- sonnet (FID: 0.600), reflecting Anthropic's continuous improvements in model architecture opti- mization and training data quality enhancement. Particularly noteworthy is Claude-4-soneet's per- formance in retrieval tasks (R@ 10: 23.96%) showing significant improvement compared to carlier versions, indicating progress in multimodal understanding and cross-modal retrieval capabilities in new versions.
Among GPT series models, GPT-4.1 performs well retrieval tasks @R@ 10: 25.00%), but GPT-4o's relatively lower performance (FID: 0.582) may be related to its design tendency toward dialogue optimization rather than visual understanding tasks. This performance difference reveals the impact of different model optimization objectives: models specifically optimized for visual understanding typically perform better in image-text matching tasks, while dtalogue-optimize models may have advantages in generation fluency but are relatively weaker in precise visual understanding.
Notably, open-source models demonstrate competitive capabilities comparable to closed-source commercial models in certain dimensions. Qwen2.5-VL-7B performs excellently in FID metrics (0.334), second only to Gemini-2.5-flass reflecting Alibaba's 's technical strength in VLM architec- ture design. More importantly, this model achieves 0.217 in lexical similarity, significantly out- performing most closed-source models, indicating the potential of the open-source community in specific task optimization. InternVL's strong performance in CLIP scores (0.771) demonstrates its capabilities in semantic understanding, benefiting from its innovative vision-language interaction mechanisms and large-scale pretraining strategies.
The consistent improvement of reinforcement learning training versions compared to supervised fine-tuning versions (7B-RL vs 7B-SFT: FID improvement 1.2%, R@ 10 improvement 2.0%) demonstrates the effectiveness of the GRPO method in archacological VQA tasks. This improve- ment is particularly evident in lexical similarity metrics, with the 7B-RL model achieving 0.276 compared the SFT version's 0.272, indicating that reinforcement learning training effectively improves the model's 's mastery of archaeological professional terminology.
Comparing the performance of models with different parameter scales, we observe that larger mod- els generally perform better in most metrics. The performance improvement of Vase VLs-VL com- pared to VLM (FID: 0.328 vs 0.363) is significant, indicating clear advantages of larger model capacity. In lexical similarity, the 7B-RL model achieves 0.276 compared to the 3B-RL ver- sion's 0.245, demonstrating that larger models have superior capabilities mastering archaeological terminology. However, 3B models still show competitive performance in other metrics, indicating good practical value resource-constrained scenarios.
Comprehensive analysis shows that specialized training can effectively compensate for disadvan- tages in model scale. Our VaseVLM-3B-RU (FID: 0.363) outperforms general models with larger parameter scales in multiple metrics, such its performance in retrieval tasks (R @ 10: 17.71% ap- proaching Dwen2.5-VL-7BB over pure scale expansion. The evaluation results further reveal inherent characteristic differences of different model architectures when processing 3D visual understanding tasks. Transformer-based models generally perform excellently in semantic understanding, while specialized 3D models have structural advantages in geometric feature capture. Our hybrid training strategy successfully com-
18.75%, demonstrating the advantages of task-specific optimization
21

---

VaseVQ Benchmarking 3D VLMs on Ancient Greek Pottery
bines the advantages of both aspects, enhancing the perception of 3D geometric structures while maintaining semantic understanding capabilities.
A.8 ARCHAEOLOGICAL QUALITATIVE ANALYSIS
Beyond quantitative metrics, conduct qualitative analysis to demonstrate that VLM has genuinely learned specialized archaeological knowledge rather than merely memorizing patterns. We compare VLM- with Gemini-2.5 across three representative cases.
Case 1: Red-Figure vs Black-Figure Classification
"Athenian Red-Figure Cup. e. 500-450 BCE, depicting youth wreathing an altar; Detroit Institute Arts."
Ground Truth:
Gemini 2.5 Flash (R@ "Athenian black-figure kylix, c. 550-500 BCE, with figural decoration; Attica."
Vase B-B---L (R@1): "Athenian red-figure cup, c. 500-450 BCE, depicting a youth at an altar; Detroit Institute Arts."
Gemini's R@ error (Black-Figure vs Red-Figure) represents a fundamental technical classification error, not minor detail difference. Black-figure and Red-figure techniques represent distinct histor- ical periods separated by 50-100 years. This error is archacologically unacceptable, as it misleads researchers about chronology and artistic development. VL correctly identifies the technique, demonstrating learned archaeological knowledge.
Case 2: Vessel Type Identification (Hydria Amphora)
"Athenian black-figure hydria, 525-475 BCE, depicting Herakles, Dionysos, Hermes, and Athena; Munich Collection." Gemini 2.5 Flash (R I): "Athenian black-figure amphora, c. 550-500 BCE, depicting mythological scene with multiple figures."
Ground Truth:
VaseVLM-7B-RL (R@) I): "Athenian black-figus hydria, c. 525-475 BCE, depicting Herakles, Dionysos, Hermes, and Athena."
The Amphora Hydria distinction critical: these vessels served different functions in ancient Greek society (storage vs water-carrying), leading to different morphological features. Misidentifi- cation propagates errors understanding vessel function, daily life practices, and museum classifi- cation. Vase correctly identifies the vessel type and associated mythological figures, indicating genuine archacological understanding.
Case 3: Descriptive Style and Generalization
Ground Truth: "Athenian black figure amphora, e. 550-500 BCE, depicting figures."
Gemini 2.5 Flash (R@1); "Athenian figure amphora, c. 550 500 BCE, depicting figures." "
VLL-BBLL (R@1); 1): "Athenian black-figure lekythos, c. 500-450 BCE, depicting a woman with a lyre; National Museum, Warsaw."
VaseVLM's answer differs from ground truth, indicating it has developed its own descriptive style emphasizing decorative patterns and professional terminology, rather than memorizing exact swers. This demonstrates genuine archaeological understanding and generalization capability.
Summary These qualitative cases demonstrate that in specialized expert domains like archacol- ogy, R@l precision is more critical than R@ 10 recall. Archacologists require accurate first answers to avoid propagating fundamental errors in research and classification. Vase superior R @ performance reflects genuine domain expertise rather than statistical pattern matching.
A.9 QUALITY COMPARISON ACROSS VLMS
Figure 7 illustrates differences in text generation performance across VLMs the dataset for QA and captioning tasks. Figures 7a and 7b illustrate QA quality comparisons across multiple VLMs, while Figure 7c shows caption generation quality comparison.
22

---

VaseDD Benchmarking 3D VLMs on Ancient Greek Pottery
Wert
Ver ddeeect
The Sear collrdd
"poaay?
CDT
dng celaggr loagl
youu chanti dioutile
(a) QA quality example. Figure 7: Examples of QA and captioning quality comparisons across different VLMSs. (a) and (b)
(b) QA quality example.
(c) Caption quality example.
show QA cases, while (c) figure illustrates captioning case.
Table 7: Data Processing Results Across Cultural Heritage Domains.
Domain
Initial
Filtered
3D Models
Retention
QA Pairs
Ancient Greek Pottery
30,000
3,880
664
2.2%
4,460
Chinese Bronze
100
73
52
52.0%
312
Ancient Greek Sculpture
100
63
58
58.0%
348
Table 8: Domain-Speciff RLVR Dimensions.
Domain
Reward Dimeusions (weights)
Ancient Greek Pottery
Fabric (0.20), Technique (0 (0,20), Shape (0. 15), Dating (0.15), Decoration (0. (0,20), Attribution (0. 10)
Chinese Bronze
Casting Material (8.18), Technique (0.22), Dating (0.15), Decoration (0.18), Preservation
(0.12)
Ancient Greek Sculpture
Clay Type (0.30), Dating (0.20), Style (0.15), Decoration (0.20), Excavation Site (0.15)
Table Performance on New Domains.
Domain
Method
FIDJ
CLIPt
R 101
R@5t
R@ 11
Lex. Sim.f
Qwen2.5-V1.-7B (Baseline)
0.356
0.732
16.68%
8.33%
3.23%
0.227
Chinese Bronze
BronzeVL.M-3 M--- RL
0.368
0.724
15.60%
8.47%
2.85%
0.216
Bronze
0.324
0.752
20.83%
10.50%
3,50 %
0.274
Owen2.5-VI.-78 (Baseline)
0.342
0.731
18.47%
8.83%
2.17%
0.235
Greek Sculpture
SculptureVI. ML--3B-RM-R
0.356
0.696
16.67%
9.37%
2.13%
0.228
SculptureVL.M-7B
0.337
0.748
19.53%
11.25%
3.31%
0.263
A. PIPELINE GENERALIZATION ACROSS CULTURAL HERITAGE DOMAINS
To validate the generalizability of our methodology beyond ancient Greek pottery, we conducted supplementary experiments on Chinese bronze artifacts and ancient Greek sculptures. Both domains share similar characteristics (complex geometric structures, rich decorative patterns, established ar- chacological classification systems) yet differ in domain-speciff knowledge requirements.
Experimental Setup
For each domain, customized the RLVR reward dimensions to reflect domain-specific archacological knowledge while maintaining the core pipeline structure. Table presents data processing results across three domains.
Domain-Specific Reward Dimensions Table & shows the customized RLVR dimensions for each domain. While maintaining six semantic dimensions, adjusted weights and focus areas based on archaeological significance.
Performance Results Table Bemonnddemssssstees consistent improvements across domains. Vase VLM- 7B-R achieves 8.4-52.5% improvement R@ accuracy and 11.9-20.7% improvement lexical similarity over baseline models.
23

---

Vase3D Benchmarking 3D VLMs on Ancient Greek Pottery
Input
3D-Vqa
Query: Please give Chinese Bronze
caption, these Dimensions: Casting Material, Technique, Dating, Decoration, Preservation State, Provenance
Qwen2.5-VL.-7
Banun
BCE, aibreviasd panduanar,
BronzeVLMM-
Zaru broaze dag.o boeriag bog ipscc Shassibard
Query: Piease the Chinese Broeze caption, in these Dimensions: Casting Material, Technique, Dating, Decoration. Preservation State, Provenance
Qwen2.5-VL..7B
Warring States breese mitrre, c-45 cas BCG, talid with 300 300-ard bymaa Qu datar lieas the fad."
BronzeVLMM-BB
wed-- Zhoe Sharrt
150word gromlag licking gramation
Query: Please the Chinese Bronze caption, in these Dimensions: Casting Material, Technique, Dating, Decoration, Preservation State, Provetance
Qwen2.-VVL-7B:
Basoro Zhou een ansi k... 500 80- high, stumpp od with 2ieharaciz petee 's markc Hemen well depeel.
BronzeVLM-7B:
Watt-Wettre Zara besnso dog DCE, za high, cart with 250 macritten on roysl rowadd;
Figure 8: VLM Generalization Chinese Bronze Artifacts. The example demonstrates cessful adaptation to domain-speciffe archacological knowledge through customized reward dimen- sions for casting material and preservation state.
Qualitative Analysis Figures and show VQA examples from Chinese bronze artifacts and ancient Greek sculptures, respectively, demonstrating the model's ability to adapt to domain-specife specific archaeological features.
These results validate the generalizability of our pipeline across diverse cultural heritage domains through flexible reward dimension customization, demonstrating applicability beyond ancient Greek pottery.
24

---

Vase3D Benchmarking 3D VLMs on Ancient Greek Pottery
Input
3D-Vqa
Query: Please give the Ancient Greek Sculpture caption, Dimensions: Clay Type, Dating, Style, Decoration, Excavation Site
Owen2..-VVL--B.
Pestoli can his teana OCE, Selmaitio boceggo siyl dent quirer, on iegis Snnd Piema
SculptureVI.M.
Partan eaarbl Achans 150 nCt, Ramas durdsnng after Taphrazor: giffns, wigiewts gregensto:; foand Ioly.
Query: Please give the Ancient Greek Sculpture caption, these Dimensions: Clay Type, Dating, Style, Decoration, Excavation Site
Qwen2.5-VL.7B.
Carrice coatlla Her Jud live Helesicio partibes poks, vell sn eneegis found Time 1896.
SculptureVLMM-7B
Theine-mmebli Albea, lat CS, Kezae www.iiiiiniiiiieeeeeee ole Credle, kcanst, aggi pergrecios.el red
Figure 9: VLM Generalization on Ancient Greek Sculptures. The example demonstrates suc- cessful adaptation to domain-specifi archaeological knowledge through customized reward dimen- sions for clay type and excavation site.
25


Reviews:
[{"review_id": "t7UIp4hITR", "reviewer": "Reviewer_zuLu", "summary": "This paper represents an interesting domain which is modeling 3D cultural heritage objects. The data construction pipline is clear and various data analysis prove the high quality of this dataset. The proposed baseline is interesting and its performance is impressive through extensive experiments.", "questions": "See weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The author introduce a novel dataset about cultural heritage. This domain is interesting and has many cultural and historical motivation.\n- Experimental results show that the proposed baselines is effective.", "weaknesses": "- The authors did not indicate what is the main characteristics of this domain that distinguish it from other domain in the same VQA task.\n- The propose baseline is somewhat general. I cannot see which module or components are designed particularly for modeling the particular features of images in the specialized cultural heritage. Therefore the factors of giving VASEVLM performed better other VLMs are unclear.\n- The authors only evaluated large models but not standard neural networks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nhXWhhYN8E", "reviewer": "Reviewer_WHvE", "summary": "This paper introduces dataset, VaseVQA-3D, a visual question answering (VQA) dataset for Greek pottery. The authors mention that there is a lack of datasets catering to cultural heritage artifacts. The paper makes the following contributions:\n1. A data construction pipeline for transforming 2D images of vases into 3D models. The resulting dataset contains 664 3D models with over 4,000 associated question-answer pairs.\n2. Fine tune a VLM (VaseVLM) for visual question answering in this domain.", "questions": ".", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper presents a well structured and technically sound pipeline for constructing 3D models out of 2D images and using them to fine tune a VLM. The end to end design could potentially be reproduced for other similar applications.\n\nThe paper expands the areas of application of language models by adding a dataset of 664 3D models of Greek pottery and associated question answer set.", "weaknesses": "The paper focuses exclusively on ancient Greek pottery and it is not clear why this domain is chosen over the others. While this is a valuable contribution, and brings AI to a new domain, maybe the authors could have provided a motivation as to why this over the other possibilities?\n\nThe dataset generation pipeline is composed of standard modules at each step. This seems to be a great software project, rather than a research project.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "STSd0swYSZ", "reviewer": "Reviewer_c3cP", "summary": "The paper introduces\n1. VaseVQA-3D: A 3D Visual Question Answering dataset for ancient Greek pottery analysis. It aims to bridge the data scarcity and domain knowledge gaps for VLMs in cultural heritage. The dataset contains 664 high-quality 3D vase models generated from 2D images via a three-stage filtering and 2D-to-3D conversion pipeline, along with 4,460 enhanced question-answer pairs.\n2. The work also proposes VaseVLM, a VLM fine-tuned for this domain, which achieves a significant improvement of 12.8% on recall at top 1 and 6.6% on lexical similarity over prior state-of-the-art models on the new benchmark (VaseVQA-3D).", "questions": "## Justification for 3D Generation Selection\nThe selection of TripoSG over Hunyuan3D is justified primarily based on achieving \"more realistic results with better vase model quality\" (visual assessment). However, the full paper already shows a trade-off in the quantitative geometric and visual metrics.  \n**Question**: Given that the paper provides quantitative metrics, why was subjective visual quality prioritized over the composite quantitative scores in selecting the final 3D generation method?  \n**Suggestion**: To provide a more robust defense of this subjective choice, the authors should quantify the visual assessment. This could be achieved by including a small-scale user study (e.g., 5-10 archaeologists or experts) to formally score the visual fidelity of the TripoSG and Hunyuan3D models on the VaseEval set. This would turn a subjective claim into a defensible metric.\n\n## Generalizability and Overfitting Concerns\nThe final dataset is quite small and synthetic which raises a primary concern about the model's generalizability and risk of overfitting to the specific synthetic distribution.  \n**Question**:\n1. Could the authors provide additional experimental evidence to demonstrate the robustness of VaseVLM against overfitting?\n2. Have the authors performed any zero-shot testing on a small set of real-world 3D vase models (outside the VaseEval set) to test generalization beyond the synthetic data?\n\n# Suggestion\nThe general-purpose Gemini 2.5 Flash still holds a lead on broader retrieval metrics R@10 and overall generative quality (FID), even though the fine-tuned model shows significant gains in high-precision metrics R@1 and Lexical Similarity. The authors should reframe this discussion by emphasizing that in a highly specialized, expert domain like archaeology, precision R@1 is more critical than recall R@10. To fully validate the gain in R@1 as the true measure of success, the authors should include a qualitative error analysis in their rebuttal. This analysis should demonstrate a case where Gemini's top answer is plausible but turns out to be archaeologically inaccurate, and contrast it with a case where the specialized VaseVLM provides the exact, technical R@1 answer.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "## Originality and Significance\nWhile there exist other 3D VQA datasets, this paper introduces the first benchmark to\n1. Focus on 3D artifacts within a highly specialized, long-tail cultural heritage domain (ancient Greek pottery)\n2. Require models to exhibit archaeological domain knowledge for answering questions (e.g.,identifying specific manufacturing techniques, dating periods, or decorative styles)\n\n## Quality (Data Rigor)\nThe methodology for dataset construction is rigorous, featuring a three-stage filtering mechanism (ResNet-50, dual-CLIP) and a dedicated validation set (VaseEval - a small 3D models dataset) of real 3D models to ensure the quality and archaeological accuracy of the synthetic data.\n\n## Quality (Model Performance)\nThe fine-tuned model, VaseVLM-7B-RL, demonstrates that specialization is highly effective for high-precision, domain-specific tasks. It shows\n1. High-Precision Accuracy: The model shows a 60% relative improvement on R@1 over the powerful general-purpose Gemini 2.5 Flash, which is crucial for expert domains.\n2. Domain Expertise: The significant lead in Lexical Similarity confirms that the fine-tuning successfully instilled the necessary specialized archaeological terminology and domain-specific knowledge.\n\n## Clarity\nThe design of the full pipeline (Figure 5) and the experimental setup are presented clearly and logically.", "weaknesses": "## Data Scale and Synthetic Nature\nThe final dataset size of 664 unique 3D models is small. These 3D assets are also synthetic, meaning they were generated from 2D images. This limited, synthetic nature carries a risk. The model might end up overfitting to the specific style of the generated data. This could, in turn, limit how well the VaseVLM works on real-world archaeological artifacts.\n\n## Trade-off in General Capability\nThe fine-tuned model excels on highly specialized metrics like $\\text{R@1}$ and Lexical Similarity. However, the smaller VaseVLM-7B-RL still lags behind the massive, general-purpose Gemini 2.5 Flash. Specifically, it performs worse on broader retrieval metrics like R@5 and R@10. This shows a core limitation of fine-tuning a small model. Large, general foundation models still have an advantage because their sheer scale allows them to encompass a wider pool of plausible information.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Yy9t3VdzZS", "reviewer": "Reviewer_EdXQ", "summary": "The paper proposes a dataset and benchmark for 3D visual question answering on ancient Greek pottery. It develops a VLM model that demonstrated improved performance on vase artifact analysis.", "questions": "I think the contribution of the paper would be more significant if the proposed approach is demonstrated to be effective and efficient on several different domains rather than just one. Also I'm curious why choose visual question answering task on this dataset. The VQA task is designed to test an AI's ability to reason with both visual and text information, but the quality gap that motivated the paper just seemed like a regular \"domain expertise\" gap that can be covered with more data in the specific domain. Constructing a VQA task on a long-tail distribution data seems to defeat the purpose of the task.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "- The paper addresses digital heritage presentation and proposes a dataset and benchmark for AI understanding on ancient potteries, which contributes to cultural protection.", "weaknesses": "- The method proposed was restricted to vase analysis, and does not generalize to other domains.\n- There's very little novelty in the proposed approach -- it seems to be a great but standard engineering effort to piece together the pipeline, rather than a significant research effort.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zhrAXxoao6", "reviewer": "Reviewer_UFR6", "summary": "In this paper, authors highlight the issue of state-of-the-art vision-language models being unable to perform well on specialized cultural heritage domains like 3D vase artifacts. They point out that existing models face severe data scarcity issues and insufficient domain knowledge limitations.\nAs a first step towards formalizing these limitations, the authors introduce one such domain: ancient Greek pottery. Authors demonstrate that existing models struggle to meaningfully answer questions about items in this new dataset.\nThe authors then provide a proof-of-concept approach to train a vision-language-model (VLM) to be specialized in this domain via \"domain-adaptive training\".\n\nOne of the distinguishing features of the present work is its 3-dimensional nature. Previously, the VaseVQA provided flattened 2-D images of vases from multiple aspects. In the present work, authors employ automated tiling methods to stitch these flattened images together to reconstruct a 3-D model to use in their ground-truth VQA data.", "questions": "- In what way are your specific metrics informative of how comparatively useful your dataset is vs. the previous one (VaseVQA-2D)?\n- The authors should discuss how easily adaptable their specific methods (both for dataset construction and augmentation as well as modeling) are for other domains should researchers in other domains choose to construct similar pipelines for their visual artifact analysis. \n- It seems from Fig. 4 that even the preferred method, TripoSG, suffers from inconsistencies and mismatches with ground truth in its 3-D model reconstruction. Treating these as starting points for further model and benchmark development seems to sow seeds of data issues. What challenges will this benchmark face going forward?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- Addressing an important, overlooked, application area for vision-language modeling via construction of an improved dataset.\n- Providing proof-of-concept modeling approach that achieves improved performance according to certain metrics.", "weaknesses": "- I would consider making the pipeline diagrams more intuitive---currently they loop in and out in unintuitive ways and the arrows are hard to follow.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tAGHggbwPE", "reviewer": "Reviewer_4ewk", "summary": "This paper addresses the problem that existing Vision-Language Models (VLMs) lack the specialized data and domain knowledge to analyze 3D cultural artifacts like ancient Greek pottery, limiting their performance in this niche area. \nTo overcome this, the authors created VaseVQA-3D, the first 3D visual question-answering dataset for this domain, by systematically filtering tens of thousands of 2D images, converting the best ones to high-fidelity 3D models, and then using this new dataset to train a specialized VaseVLM model. \nThe paper then validates its approach by conducting a comprehensive evaluation of VaseVLM against a wide range of state-of-the-art 3D-specialized, closed-source, and open-source VLMs on the new benchmark. \nThe superiority of this method is demonstrated through significant quantitative improvements, such as a 12.8% increase in R@1 accuracy and a 6.6% increase in lexical similarity over the previous best models, and is further confirmed by higher ratings from human archaeological experts.", "questions": "Please refer to the Weaknesses section.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper presents a well-structured and technically comprehensive pipeline for transforming 2D cultural heritage imagery into 3D visual question answering data, integrating filtering, reconstruction, and multimodal fine-tuning. This end-to-end design demonstrates methodological rigor and can serve as a reproducible framework for future domain-specific multimodal research.\n\n2. The work introduces one of the first benchmarks focused on 3D VQA within the cultural heritage domain. By combining structured question–answer pairs with enhanced descriptive captions and expert-validated annotations, it provides a dataset resource for studying vision-language alignment in specialized, long-tail domains.", "weaknesses": "1. The abstract and contributions claim a \"12.8% improvement on R@1 metrics and 6.6% on lexical similarity compared with previous state-of-the-art\". However, the paper does not explicitly identify which specific model from Table 3 serves as this \"previous state-of-the-art\" baseline. While the R@1 improvement can be inferred by comparing VaseVLM-7B-RL (3.52%) to the highest non-proposed models (3.12%), the 6.6% claim for lexical similarity does not clearly correspond to any specific baseline in the table. It is recommended that the authors explicitly state the baseline model used for these headline claims.\n2. The final VaseVQA-3D dataset consists of 664 3D models, with only 90 models in the test set. Conclusions drawn from a test set of this size may be subject to statistical noise and raise questions about the generalization capabilities of the models. \n3. The proposed Reinforcement Learning with Verifiable Rewards (RLVR) framework calculates rewards based on cosine similarity to ground truth content across six dimensions, using a threshold of τ=0.7. This reward design is inherently coupled with the final evaluation metrics, particularly \"Lexical Sim.\". This creates a risk that the model is not learning a deeper semantic understanding but is instead being optimized to \"hack\" the scoring function. The authors should discuss this potential for \"overfitting to the metrics\" and could strengthen their claims by providing evaluations on out-of-domain tasks or through more robust, task-based human evaluations that are orthogonal to the reward function.\n4. The descriptive captions in the dataset were enhanced using GPT-4o. Since these enhanced texts serve as the ground truth for both model training and subsequent evaluation (e.g., retrieval and lexical similarity), there is a risk of information leakage. The evaluation may inadvertently reward models that are better at mimicking the linguistic style of GPT-4o rather than demonstrating a true understanding of the archaeological content. \n5. The selection of TripoSG over Hunyuan3D was based on a comparative analysis using the VaseEval validation set, which contains only 24 ground truth models. A conclusion based on such a small sample may not be generalizable to the full diversity of vase morphologies. The paper would be more convincing if this comparison were conducted on a larger and more varied set of real 3D assets to ensure the robustness of the chosen generation method.\n6, A core methodological concern is the end-to-end process in which the dataset is self-generated, its captions are augmented by an LLM (GPT-4o), and the models are trained and evaluated against this same data ecosystem. This is compounded by the fact that the RLVR reward function directly mirrors the evaluation metrics. This tight coupling introduces a significant risk of bias, where the model's strong performance may stem from learning the artifacts and stylistic patterns of the dataset creation process itself, rather than achieving genuine \"archaeological terminology understanding\".\n\n[Minor]\n1. The paper positions itself as having significant social value for \"digital heritage preservation\". However, the results remain a \"proof-of-concept\" and do not address the practical feasibility, cost-effectiveness, or workflow for deploying this system in real-world scenarios such as museum curation, automated classification, or restoration assistance. For instance, the stated \"13.5 days for 3D generation\"  suggests significant computational cost. A discussion of these practical barriers would provide a more balanced perspective on the work's current impact.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)