PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data
Authors: Zhe Zhu
Le Wan
Rui Xu
Yiheng Zhang
Honghua Chen
Zhiyang Dou
Cheng Lin
Yuan Liu
Mingqiang Wei
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Part Segmentation
Segment Anything
Open-World Segmentation
Interactive Segmentation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Segmenting 3D objects into parts is a long-standing challenge in computer vision. To overcome taxonomy constraints and generalize to unseen 3D objects, recent works turn to open-world part segmentation. These approaches typically transfer supervision from 2D foundation models, such as SAM, by lifting multi-view masks into 3D. However, this indirect paradigm fails to capture intrinsic geometry, leading to surface-only understanding, uncontrolled decomposition, and limited generalization. We present PartSAM, the first promptable part segmentation model trained natively on large-scale 3D data. Following the design philosophy of SAM, PartSAM employs an encoder–decoder architecture in which a triplane-based dual-branch encoder produces spatially structured tokens for scalable part-aware representation learning. To enable large-scale supervision, we further introduce a model-in-the-loop annotation pipeline that curates over five million 3D shape–part pairs from online assets, providing diverse and fine-grained labels. This combination of scalable architecture and diverse 3D data yields emergent open-world capabilities: with a single prompt, PartSAM achieves highly accurate part identification, and in a “Segment-Every-Part” mode, it automatically decomposes shapes into both surface and internal structures. Extensive experiments show that PartSAM outperforms state-of-the-art methods by large margins across multiple benchmarks, marking a decisive step toward foundation models for 3D part understanding.

Published as a conference paper at ICLR 2026
PARTSAM: A SCALABLE PROMPTABLE PART SEGMEN- TATION MODEL TRAINED ON NATIVE 3D DATA
Zhe Zhu', Le Wan?, Rui Xu², Yiheng Zhang", Honghua Chen Zhiyang Dou², Cheng Lin Yuan Liu"i, Mingqiang Wei
1 NNanjing University Aeronautics and Astronautics, "Hong Kong University of Science and Technology. "The University of Hong Kong,
*National University of Singapore,
Linggaa University,
Macau University of Science and Technology
T
Figure I: We propose PartSAM, promptable 3D part segmentation model trained with large-scale native 3D data. The combination of a scalable architecture and large-scale training data endows PartSAM with strong generalization ability, enabling automatically decompose diverse 3D models, including both artist meshes and Al-generated shapes, into semantically meaningful parts.
ABSTRACT
Segmenting 3D objects into parts is long-standing challenge in computer vision. To overcome taxonomy constraints and generalize to unseen 3D objects, recent works open-world part segmentation. These approaches typically trans- fer supervision from 2D foundation models, such as SAM, by lifting multi-view masks into 3D. However, this indirect paradigm fails to capture intrinsic geometry, leading to surface-only understanding, uncontrolled decomposition, and limited generalization. We present PartSAM, the first promptable part segmentation model trained natively on large-scale 3D data. Following the design philosophy of SAM, PartSAM employs an encoder-decoder architecture which a triplane-based dual-branch encoder produces spatially structured tokens for scalable part-aware representation learning. To enable large-scale supervision, we further introduce a -hh--lopppppppooooooooo
model-in-the loop annotation pipeline that curates five million 3D shape -art pairs from online assets, providing diverse and fine-grained labels. This combi- nation of scalable architecture and diverse 3D data yields emergent open-world capabilities: with single prompt, PartSAM achieves highly accurate part identifi-
CCoresponning authors.

---

Published as a conference paper at ICLR 2026
cation, and in "Segment-Every -eeee---eer""" mode, it automatically decomposes shapes into both surface and internal structures. Extensive experiments show that PartSAM outperforms ofte-of-tthear methods by large margins across multiple benchmarks, marking a decisive step toward foundation models for 3D part understanding. Project page:
INTRODUCTION
Segmenting 3D objects into their constituent parts is a fundamental problem in computer vision and graphics. A reliable solution would benefit wide range downstream applications, including 3D asset creation, AR/VR content editing, and robotic manipulation. The key requirements of such model are generalization beyond fixed taxonomies, flexible interaction through user guidance, and robustness in open-world scenarios where novel categories and diverse part definitions are common.
Conventional approaches (Qi et al. 2017 Mo et al., 2019 Wang et al. 2019a Zhao et al. 2021) have made progress by training networks 3D datasets with predefined part taxonomies. However, these datasets are small and glosed-world in nature: for example, chairs may only be annotated with seat, back, and legs, while cars are annotated with wheels and doors. Models trained under such assumptions cannot generalize to unseen categories or alternative defimitions of part granularity. As result, they perform well within benchmarks but fall short when deployed real-world scenarios with open-world queries.
To address these limitations, subsequent works (Liu al. 2023 Zhu al. 2023 Liu et al. 2024a 2025 Ma et al. 2024) transfer knowledge from extensively trained 2D foundation models. For exam- ple, SAMPart3D (Yang et al. 2024b) lifts multi-view segmentation results SAM (Kirillov et al.) 2023) to 3D space, but this process requires time-consuming per-shape optimization. More recent methods (Ma et al. 2024 Liu et al.) 2025 Zhou et al. (2025) adopt a feed-forward paradigm, training 3D networks with supervision from SAM's 2D masks and directly inferring segmentation results. For instance, PartField (Liu et al. 2025) achieves impressive performance by training 3D feature field through contrastive learning, with segmen- tation results obtained by clustering in the fea- ture space. Despite these advancements, exist- ing methods still lag significantly behind their 2D counterpart, SAM, due to two key limitations: (1) Clustering- based segmentation approaches (Yang) al. 2024b Liu et a 2025) lack the user-centered
controllability inherent to SAM, often resulting in
fragmented parts without carefully tuned cluster number. (2) These methods rely heavily on super- vision from SAM's multi-view 2D segmentation, which restricts their capabilities to the object's ' sur- face, thus limiting their ability to achieve compre- hensive 3D geometric understanding and to capture interior structures (see Figure 2).
PartField
Ours
Figure 2: The SOTA method PartField (Liu et al. 2025) fails to segment the interior struc- ture of 3D shapes.
In this paper, we propose PanSAM, a promptable part segmentation model trained natively on large-scale 3D data, which addresses the above challenges in three aspects. First, our network adopts a segmentation paradigm inspired by SAM, featuring a prompt-guided encoder-decoder architecture. Such a SAM-like decoder facilitates more accurate grouping of semantically consistent parts than clustering-basee segmentation, the prompt-guided training provides more precise supervisory signal. Moreover, as the segmentation explicitly controlled by prompts, supports flexible interactive automatic segmentation during inference.
Second, we propose an effective encoder that can scale to large 3D datasets. Specifically, we encode shapes into triplane-based feature field (Chan et al. 2022) using a dual-branch transformer (Vaswani et al. 2017). To better leverage existing priors, we leverage one learnable branch scale to large 3D datasets, while the other frozen path preserves 2D priors of SAM leamnd through contrastive learning (Liu et al.) 2023). Additionally, by incorporating input attributes beyond just coordinates, the model enhances the representation of local shape details.
2

---

Published as a conference paper at ICLR 2026
Third, we propose scalable pipeline for curating a large-scale part segmentation dataset. We extract part supervision from extensive 3D assets (Deitke et al. 2023b a) by leveraging artist-annotated scene graphs and connected components. To ensure sufficient diversity in the supervision, design model-in-the-loop strategy that iteratively refines and scales annotations, ultimately producing over five million native 3D shape-part pairs.
Compared with existing methods YYng et al. 2024b Liu et al. 2025 Zhou et al. 2025 Ma et al. 2024), PartSAM is the first to simultaneously achieve flexible controllability, feed-forward inference, and scalable performance within native 3D framework. The synergy of a scalable architecture and large-scale 3D training data equips PartSAM with generalizable interactive capabilities. When given only single prompt point, PartSAM surpasses Point-SAM (Zhou et al.] (025) by over 90% in open-world settings. Building upon the impressive single-prompt segmentation quality, we further propose "Segment Every Part" mode to automatically segment entire shapes. Experiments show that PartSAM significantly outperforms state-of-the methods in this task. The main contributions of this work can be summarized as follows.
We introduce PartSAM, the first scalable feed-forward promptable 3D part segmentation model trained with native 3D supervisions, enabling flexible open-world segmentation at inference.
We design an effective branch encoder that represents 3D shapes as robust part-aware feature fields, enabling effective scaling to native 3D supervision, while simultaneously retaining the powerful 2D priors derived from SAM.
We propose a model-in-ther in-the-loop annotation pipeline to mine part supervision from large- scale 3D assets, scaling the training data to millions of shape-part pairs.
Pa achieves state-of-the-ard performance on multiple open-world part segmentation benchmarks and demonstrates broad applicability, providing a strong foundation for future research in 3D part understanding.
RELATED WORK
2.1 CLOSED-WORLD 3D PART SEGMENTATION
Early learning-bass based methods QQi et al. 2017: Thomas et al. 2019 Wang et al., 2019b; Zhang & Wonka 2021) typically formulate part segmentation as point-level semantic or instance classification on 3D shapes. These approaches are commonly trained on ShapeNet-Part (Yi et al. 2016 and PartNet (Mo et al. (011)). The limited diversity of these datasets in terms of object and part categories, however, constrains their generalization to unseen data.
2.2 LIFTING 2D FOUNDATION MODELS FOR 3D PART SEGMENTATION
To address the limitations of existing small datasets and enable open-world part segmentation, a growing body of work leverages the strong priors embedded in 2D foundation models (Radford et al.
2021;
Li et al. 2022; Kirillov et al., 2023 Ravi et al.) Oquab et al. 2024). One research direction
focuses on dextedriven approaches (Abdelreheem et 2023 Liu al. 2023 Zhu ct al. 2023
Garosi et al. 2025). ) which query target segmentation based on the part name in the feature space of
languanl models (Radford et al. 2021
al. 2022).
Another prominent line of work explores transferring knowledge from SAM (Kirillov et al. 2023
Ravi al.). Some methods directly project SAM's multi-view segmentation masks into 3D space by exploiting relationships across views (Liu et al. 2024a| Zhong et al. 2024 Tang et al. 2024). For example, SAMesh (Tang et al., (024) employs community detection algorithm to merge multi-view predictions into coherent 3D segments. Other approaches instead distill SAM in the feature space (Yang et al., 2024b; Lang et al. 2024). SAMPart3D YYnng et al.) (024b) introduces a ocale-conditioned MLP to handle ambiguity in SAM-generated masks, while iSeg (Lang al. 2024 adopts a two-stage pipeline that distills SAM's ' features for 3D interactive segmentation.
Despite the progress, these lifting based methods still face inherent challenges. In practice, they often require computationally expensive post-pros processoess for each shape during inference, which restricts their scalability in downstream applications.
3

---

Published as a conference paper at ICLR 2026
2.3
FEED-FORE FORDARD MODELS FOR 3D SEGMENTATION
Motivated by the scaling laws observed in modern large models, recent efforts have aimed to train feed-forward 3D segmentation models that directly predict open-world results inference time. Prior work on scene segmentation (Takmaz et al. 2023 Wang et al. 2025; Diang et al., 2024 Peng et al.) 2023 Yang al. 2024a) mainly focuses on aligning 3D representations with text embeddings in Vision languagee feature space through 2D-3D aggregation. While these methods demonstrate strong performance on scene-level tasks, extending them to object part segmentation considerably more challenging, as it requires fine-graines reasoning about part-level geometry.
For part segmentation specifically, Find3D (Ma al. 2024) adopts training strategy similar to these scene-level methods. To address the data scarcity problem, uses SAM and Gemini to generate 2D part annotations with associated textual labels. PartField (Liu et al. 2025) instead represents shapes continuous feature fields and trains transformer-bast ambiguity-agnostic contrastive loss. Despite its impressive performance, the feature space clustering of PartField relies extremely on the connectivity of the input mesh to get high-quality segmentation. For generated meshes (Lai et al., 2025 Xiang et al. 2025) Zhang et al.) 2024, Liu et al.) 2024b Long al. 2024), where such connectivity is absent, performance drops significantly (see Fig. [8).
feed-forward network with an
Zhou et al.
2025 further propose Point-SAM, a 3D analogue of SAM trained on SAM-annotated
multi-view renderings of ShapeNet, but the limited encoder scalability and training data diversity restrict its generalization to open-world settings. Consequently, it does not fully unleash the potential of the SAM architecture for part segmentation.
More broadly, all these existing approaches heavily rely on SAM-generates 2D masks for data construction, which fundamentally constrains their capacity to capture intrinsic 3D geometric structure and predict meaningful interior parts. Distinct from these methods, we introduce native 3D foundation model for part segmentation, trained on large-scale shape-pane pairs to enable scalable, geometry-aware, and controllable understanding beyond 2D supervision.
FPS
Grouping
F
Mask Decoder
T Towr F
Encoder
T
IoU Head
Pin
Feature Field
Figure 3: Overview of the PartSAM model. The input shape P.n. first encoded into a continuous feature field. Point patches sampled from P.n. query this field obtain input embeddings Fe, while prompt points are mapped into prompt embeddings Fp. Both F. and Fp are fed into the mask decoder, where the learnable output token Tout generates multiple segmentation masks. An additional IoU token Tim is used by the IoU head to estimate the quality of each mask.
METHOD
Our goal to develop a scalable framework for segmenting arbitrary 3D parts, analogous to SAM (Kirillov et al.) 2023)) in the image domain. The architecture consists of two main components: an input encoder that transforms 3D) shapes into structured feature embeddings, and prompt-guided mask decoder that predicts segmentation masks conditioned on user prompts. We primarily consider 3D click points as prompts, including both positive and negative ones. Formally, given 3D shape represented where each point may include 3D coordinates, surface normals, and optional RGB color (i.e., dn = 9), and set of user prompts Pprompt C RRN, PartSAM first transform the input shape into feature embeddings F, through the input encoder. These are combined with prompt embeddings F, in the mask decoder to generate binary segmentation mask Mout- To fully realize this design at scale,
overview of PartSA shown in Figure 3
point cloud P... C R...xd.,
4

---

Published as a conference paper at ICLR 2026
further introduce data curation pipeline that mines diverse and fine- fine-graine -rgiindd 3D part annotations from large-scale assets, enabling PartSAM to generalize across open open-world scenarios. Additional details are provided in Appendix A.1
3.1 INPUT ENCODER
The encoder, a key component for scalable training, extracts feature embed- dings Fe from Pon for the mask decoder. Unlike prior approaches (Zhou et al.] 2025) that rely solely on point cloud networks, our approach encodes parts within a continuous triplane (Chan et al. 2022) feature field.
XYZ
Normal, RGB
Zero Conv
As illustrated in Figure we implement the encoder as a dual-branch network to effectively leverage priors from different modalities. In each branch, the input points are first transformed into three 2D planes using PVCNN (Liu et al., 2019), followed by a projection operation. These axis-aligned planes then processed by transformer (Vaswani et al., 2017), allowing them to query feature vectors for any given 3D coordinate. During training, initialize each branch with pre-trained weights from PartField (Liu et al. 2023), keeping one branch frozen and the other one learnable. The frozen branch incorporates rich 2D knowledge distilled from SAM (Ravi et al.). learned via contrastive learning (Liu et al. (2025). while the learnable branch adapts and learns new representations of native 3D parts from our training data. Since our SAM-like architecture can not be directly trained with incomplete 2D masks, this dual-branch design enables effective scaling to native 3D supervision, while simultaneously retaining the powerful 2D priors derived from SAM. Moreover, the learn- able branch accepts additional input attributes beyond coordinates (i.e., normal and RGB) through zero convolution layer (Zhang et al.) 22023.
PVCNN *
PVCNN
Transformer *
Transformer
Triplanc
Figure 4: Architecture of our dual-branch en- coder. Each branch is initialized with pre- trained weights of Liu et al. (2025).
further enhancing the representation of shape details. Finally, the outputs of the two branches summed to produce a continuous feature field.
To obtain tokens for the mask decoder, adopt the sampling sumpling-and-groon and-grouping strategy of Qi al. (2017), where N₂ centers are selected via farthest point sampling (FPS), local patches are formed around each center, and features sampled from the triplane are aggregated with shared MLPP
F MLP (d(p) p (FPS((P... No))) NeXe
(1)
where ((-) denotes feature sampling from the triplane and N(.-) means KNN-based local patches.
3.2 PROMPT-GUIDED MASK DECODER
Unlike the clustering- methods (Yang et al., 2024b; Liu et al. 2025), the prompt-guides decoder directly produces segmentation masks explicitly controlled by user prompts, enabling flexible interactive or automatic segmentation during inference. Specifically, it maps user prompt points Prrompt and input embeddings F. to binary segmentation mask Mout- Prompt points are first encoded into embeddings F, by sampling features from the continuous feature field and combining them with position embeddings. For multi-round interactions, mask logits from previous rounds are incorporated as additional prompts and directly added to F, refine the predictions.
For mask decoding, two special tokens are introduced: an output token Tout that generates segmen- tation masks, and an loU token Tim.. that estimates mask quality. These tokens, together with the prompt embeddings Fp. are concatenated and attend bidirectionally to the patch embeddings F, using two-way transformer (Kirillov al. 2023):
F' = CrossAttn F.. <> [Fp; Touts Time: Tioul)
The refined embeddings F. are upsampled to the input resolution using distance-based interpolation. Mask logits are subsequently computed through a point-wise dot product between the upsampled embeddings and the refined output token T" Per-point foreground probabilities are then obtained by applying sigmoid function, and the final binary mask Mout is generated by thresholding these probabilities.
5

---

Published as a conference paper at ICLR 2026
To handle the inherent ambiguity of 3D part boundaries, the decoder follows the parallel decoding strategy of SAM. When a single prompt is given, multiple output tokens (three in our implementation) generate diverse candidate masks. In parallel, additional IoU token is trained to estimate the overlap between each predicted mask and the ground truth, providing a confidence score that guides the selection of the final output.
3.3 TRAINING STRATEGY
Following SAM Kirillov et al. 2023). during training, we simulate interactive segmentation by first sampling prompt from the center of the ground-truth mask and then iteratively sampling subsequent prompts from prediction error regions for 9 iterations. The IoU prediction supervised with the
MSE loss. The segmentation masks are supervised with a combination of focal loss (Lin et al. 2017) and dice loss (Milletari al. (016). For each ground- truth mask, the triplet contrastive loss
Liu
et al. (2025) is also used to strengthen the representation ability of the encoder.
L = focal (Mout: Mgt + aL (Mout> Mat) + LioU + X Leriplet,
(3)
where Mgt is the ground-truth mask and a and À are weighting coefficients for the loss terms.
3.4 AUTOMATIC SEGMENTATION
Similar to SAM (Kirillov et al. 2023), PaRtSAM demonstrates an emergent ability to autonomously segment complete shapes after training. To facilitate this, we propose a novel "Segment Every Part" pipeline for utilizing
decoder to automatically segment every meaningful part for shape. Concretely, we first sample N3 points from each shape using FPS, treating each sampled point as an independent prompt. This yields y candidate masks together with their predicted IoU scores. The candidates are then refined in two stages: (1) discarding masks with low predicted IoU, and (2) applying Non-Maximum Suppression (NMS) based point-level IoU to eliminate redundant masks, with the predicted IoU serving as the confidence score. Finally, mesh faces are assigned labels according to the point-level predictions sampled on them. During this process, the threshold value of NMS T is set as a hyperparameter to adjust the granularity of automatic segmentation.
3.5
DATA CURATION
Training a scalable and generalizable 3D segmentation model requires large-scale, high-quality part annotations. To this end, we construct such supervision through two complementary stages:
Integrating existing part labels. We primarily cu- rate part annotations from Objaverse (Deitke et al., 2023b) and other licensed datasets. For existing part segmentation datasets like PartNet (Mo et al., 2019) and ABO (Collins et al. 2022). directly use the provided ground-truth labels. For artist-created assets, like those in Objaverse, GLTF scene graphs serve as natural supervision when available, while assets with a single geometry node are decomposed into connected com- ponents. Then, to improve annotation reliability, we discard shapes with fewer than three or more than fifty parts and filter out components that are extremely small or excessively large. This process yields approximately 180k shapes with 2 million parts.
Faces: 112,346
Components: 636
Figure 5: Example of an artist-created mesh with over 600 connected compo- nents. The large number of fragmented pieces makes difficult to obtain semanti- cally meaningful parts, and such assets are excluded from direct supervision.
Most highly fragmented assets are excluded during this filtering stage, since their connected components fail to form semantically meaningful parts (see Figure (5). While these cues cannot be directly utilized for training, they motivate our next step: we first pretrain PartSAM on the data from the first stage, and then leverage the pretrained model in a model-in ineee-ii-ttthooooe thop pipeline to annotate part labels for these over over- wer-fragmented structures.

---

Published as a conference paper at ICLR 2026
Mooeel-nntth-loopp annotation. Although PartField (Liu et al. 2025) suffers from limited controlla- bility due to its clustering-baser design. it can still produce sharp masks for certain parts when mesh connectivity is well defined. We exploit this property by treating PartField outputs candidate labels and leveraging the interactive capability of PartSAM to filter out noisy segmentations. Concretely, first pretrain PartSAM the curated dataset, which, though less diverse, endows the model with basic interactive part segmentation ability. For each over- wreg-fraeeented shape discarded in the first stage, PartField generates multi-scale masks by setting the clustering number 10, 20, and 30. These masks are used as pseudo labels and passed to PartSAM, which performs 10 rounds of interactive segmentation simulation (Sec. (.3). At each step. we compute the IoU between the PartSAM prediction and the PartFiell pseudo label, denoted loU@i for the i-th iteration. A mask considered valid if satisfies either loU@1 > 60 IoU@10 > 90. The intuition is that reliable part masks should either be interactively segmented immediately with even single prompt, or be progressively refined through extended interactions. Meanwhile, we only regard a shape as valid if contains more than valid masks. As illustrated in Figure [3 this model-in-the-lowe process yields robust annotations. Through this pipeline, we expand the training corpus to 500k shapes and more than million parts, substantially improving both scale and quality.
4 EXPERIMENT
Polnt-SAN
Ours
Figure 6: Qualitative comparison with Point-SAM (Zhou et al. 2025) on interactive part segmentation. Predicted segmentation masks are shown in red. Yellow and green points denote positive and negative prompt points, respectively. Compared to Point-SAM. PaRtSAM produces more complete and semantically consistent parts, even with minimal prompts.
Table 1:
Quantitative comparison of interactive segmentation PartOhjaverse-Timg (Yang al. 2024b) and PartNetE (Liu et al. 2023). The best scores are emphasized in bold. loU@i denotes mean loU value with prompt points. We report the mean loU on instance-level labels.
Dataset
Method
loU@
loU@3
loU@5
loU@7
IoU @ 10
Point-SAM
29.4
58.6
68.7
71.8
73.9
PartObjaverse--iny
Ours
56.1
78.3
84.1
86.2
87.6
Point-SAM
35.9
68.0
75.1
77.6
79.2
PartNetE
Ours
59.5
79.3
86.5
88.3
89.9
4.1 COMPARISON INTERACTIVE PART SEGMENTATION
We compare PartSAM with Point-SAM (Zhou et al. 2025 the interactive part segmentation task
using the PartObjaverrs-TTin Tiny (Yang et al. 2024b) and PartNet-E (Liu et al. 2023) datasets. Following the experimental protocol of Zhou al. (2025), the first prompt point for each ground-trutt -tuth mask sampled from its central region. Subsequent prompt points are iteratively selected from the error regions between the predicted mask and the ground truth.
7

---

Published as a conference paper at ICLR 2026
Input
SAMesh
SAMPar33D
PartField
Ours
PartObjaverse- Tiny
Al-generated
Figure 7: Qualitative comparison of class- class-agnostic part segmentation with baselines (Tagg et al. 2024 Yang et al. 2024b) Liu et al. (025) on PartObjaverse- Tiny (Yang et al.) (2024) and Al-generated 3D models (Lai et al. 2025 Each segmented part is visualized with distinct color.
The quantitative results Table demoonsratte PartSAM's superiority over Point-SAM across varying numbers of prompt points. In particular, with only single prompt, PartSAM achieves 91% relative improvement IoU, demonstrating its ability to accurately delineate parts from just one click. This advantage further supported by the qualitative results Figure 6 PartSAM reliably produces precise and semantically meaningful parts even from a single point, whereas Soint-SAM often fails due to the limited scalability of the network architecture, thus lacking an explicit notion of arbitrary 3D parts in the open world setting. Moreover, Point-SAM dependence on 2D multi-view supervision from SAM hampers its ability to capture internal 3D structures. For instance, in the second column of Figure 6 PartSAM successfully segments the entire leg, including the occluded knee, while Point-SAM misses due to its incomplete understanding of 3D geometry.
4.2 COMPARISON OF CLASS-AGNOSSE PART SEGMENTATION
As introduced in Section 3.4 PartSAM supports automatic shape decomposition through its "segment every part" mode. We compare PaRtSAM against five state-of-the-ae art methods (Liu et al., 2023
Ma et al. 2024; Yang et al., 2024b Tang et al., 2024 Liu et al. 2025) on this task. We use the PartOOjaveese- Tiny (Yang et al. 2024b) and PartNet-E (Liu et al. 2023) datasets for quantitative
evaluation and additionally use Al-generated meshes produced by Hunyuan3D (Lai et al. 2025) for qualitative comparison. We follow the experimental protocol from Liu et al. (2025) with key modification to prevent potential label leakage from mesh connectivity. Specifically, since artist-created meshes in PartOhjaverse-Ting contain strong connectivity cues that align with ground-
8

---

Published as a conference paper at ICLR 2026
PartFleld
Ours
Figure 8: Qualitative comparison of hierarchical part segmentation with PartField (Liu et al. 2025 on Al-generated 3D models (Lai et al. 2025). Each segmented part is represented as distinct color.
truth part labels (see Appendix A.2.5), disable this connectivity information when evaluating PartField (Liu et al., 2025) and instead apply K-Means clustering directly on mesh faces. In contrast, SAMesh (Tang al. 22024,, which fundamentally depends mesh connectivity through community detection, is evaluated in its original setting. Notably, for baselines that produce multi-scale part segmentations, we evaluate performance by computing the IoU for all candidate masks and reporting the best score. For our PartSAM, multi-scale segmentations are obtained by varying the NMS threshold value T' at 0.1, 0.3, 0.5, and 0.7, respectively.
Table 2:
Quantitative comparison of automatic segmentation on PartObjaverse-Ting (Yang al. 2024b) and PartNetE (Liu et al., (2023). denotes that PartField is evaluated with K-Means clustering without mesh connectivity information. We report the mean IoU on instance-level labels.
Dataset
PanSLII
Find3D
SAMPart3
SAMesh
PartField
Ours
PartObjaverse- PartNetE
31.5
21.3
53.5
56.9
51.5
69.5
34.9
21.7
56.2
26.7
59.1
72.4
Results Table 2 demonstrate that PartSAM consistently surpasses all competing methods, achieving over 20% IoU improvement over the second-best approach across both datasets.
Qualitative comparisons in Figure further reveal that PartSAM produces robust part segmentations for diverse 3D shapes. SAMesh also exhibits clear boundaries but often generates over-segmented outputs, as its direct projection and merging of 2D SAM masks into 3D space fails to capture under- lying geometric structures. SAMPart3D and PartField, which rely feature clustering, encounter difficulties on complex and detailed structures. Their clustering process frequently yields fragmented or semantically meaningless parts, for example, incorrectly partitioning the body of grenade into several disjoint segments.
We also conduct a comparison with PartField in the context hierarchical multi-scale segmentation on Al Al-generated meshes (Figure 8). PartSAM delivers finer gner-grained results with sharper boundaries, highlighting its capability produce high-quality segmentation across scales.
In Figure we further compare PartSAM with baselines on diverse Al-generated meshes with interior or occluded parts. SAMesh benefits from inheriting SAM's billion-scale mask priors, enabling it to capture extremely fine surface details and produce sharp boundaries (e.g., the car exterior). However, this 2D-driven granularity may also over-fragment large but semantically coherent regions (handbag), and, more importantly, it struggles when parts are occluded in most rendered views-for example, SAMesh cannot reliably recover the objects inside the handbag, the seats and steering wheel inside the car, the structures hidden beneath the robot's 's cloak. In contrast, PartSAM operates directly on native 3D geometry and learns multiscale, semantically meaningful decompositions, producing coherent part groupings for both visible and partially hidden structures.

---

Published as a conference paper at ICLR 2026
Input
SAMesh
PartField
Ours
Figure 9: Qualitative comparison of class-agnostic part segmentation with baselines (Lu et al. 2025 Tang al., 2024) on AI- Al-generated 3D models (Lai et al. (0255) with interior structures and occluded parts. Each segmented part is represented as a distinct color.
Overall, these results highlight the superiority of PartSAM existing methods: By replacing heuristic clustering with an automatic promptable framework and leveraging large-scale 3D training, it establishes more controllable and generalizable model with 3D-aware part understanding.
CONCLUSION AND DISCUSSIONS
In this work, we introduced PartSAM, scalable promptable model that performs part segmentation natively in 3D. Unlike prior approaches that rely on transferring knowledge from 2D foundation models, PartSAM is trained directly millions of 3D saae-paat pairs, enabling faithful under- standing intrinsic 3D geometry. Our design combines a triplane- basedd encoder with a promptable encoder-decoder architecture, supported by model-in-the-loone pipeline that supplies diverse anno- tations at scale. This synergy between architecture and data drives maat--of-thearr performance on both interactive and automatic segmentation tasks. These findings suggest that scaling with native 3D data opens new avenues for advancing pert-level understanding, and we believe PartSAM lays foundation for future exploration of richer, more generalizable 3D perception models.
Discussions. Please refer to the Appendix for comprehensive ablation studies and additional discus- sions, including applications, complexity analysis, and limitations.
ACKNOWLEDGMENTS
This work supported by the National Natural Science Foundation of China (No. T2322012, No. 62572240). was also supported by the donation from Kerry Group Limited in "30-for-30" Talent Acquisition Campaign of HKUST.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Ahmed Abdelreheem, Ivan Skorokhodov, Maks Ovsjanikov, and Peter Wonka. Satr: Zero-shot semantic segmentation of 3d shapes. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 33166-15179 2023.
Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222-1239 2001.
Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein. Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16123-16133, June 2022.
Sungjoon Choi, Qian- Yi Zhou, Stephen Miller, and Vladlen Koltun. A large dataset of object scans.
arXiv:1602.022 4881,, 2016.
Jasmine Collins, Shubham Goel, Kenan Deng, Achleshwar Luthra, Leon Xu, Erhan Gundogdu, Xi Zhang, Tomas F Yago Vicente, Thomas Dideriksen, Himanshu Arora, et al. Abo: Dataset and benchmarks for real-world 3d object understanding. Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 21126 136, 2022.
Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al. Objaverse-xl: A universe of 10m+ 3d objects. Advances Neural Information Processing Systems, 36:35799-35811 2023a.
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of anno- tated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13142-13153, 2023b.
Marco Garosi, Riccardo Tedoldi, Davide Boscaini, Massimiliano Maneini, Nicu Sebe, and Fabio Poiesi. 3d part segmentation via geometric aggregation of 2d visual features. In 2025 IEEF/CVF Winter Conference on Applications of Computer Vision, pp. 3257-3267, 2025.
Li Jiang, Shaoshuai Shi, and Bernt Schiele. Open-vocabulary 3d semantic segmentation with foundation models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21284-21294, 2024.
Alexander Kirillov. Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloc Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEF/CVF International Conference Computer Vision, pp. 4015-4026, 2023.
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.
Itai Lang, Fei Xu, Dale Decatur, Sudarshan Babu, and Rana Hanocka. iseg: Interactive 3d seg- mentation via interactive attention. In SIGGRAPH Asia 2024 Conference Papers, pp. 1-11, 2024.
Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp.
10965-10975, 2022.
Tsung- Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988, 2017.
Anran Liu, Cheng Lin, Yuan Liu, Xiaoxiao Long, Zhiyang Dou, Hao-Xiang Guo, Ping Luo, and Wenping Wang. 12r part-aware 3d reconstruction from a single-view image. In ACM SIGGRAPH 2024 Conference Papers, pp. 1-12, 2024a.

---

Published as a conference paper at ICLR 2026
Minghua Liu, Yinhao Zhu, Hong Cai, Shizhong Han, Zhan Ling, Fatih Porikli, and Hao Su. Partslip: Low-shot part segmentation for 3d point clouds via pretrained lane-language models. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21736-21746, 2023.
Minghua Liu, Mikaela Angelina Uy, Donglai Xiang, Hao Su, Sanja Fidler, Nicholas Sharp, and Jun Gao. Partfield: Learning 3d feature fields for part segmentation and beyond. arXiv preprint arXiv:2504.11451, 2025.
Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Generating multiview-coosssten images from a single-view image. In The Twelfth International Conference Learning Representations, 2024b.
Zhijian Liu, Haotian Tang, Yujun Lin, and Song Han. Point-voxel cnn for efficient 3d deep learning. Advances in Neural Information Processing Systems, 32, 2019.
Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Boig-HaiZue Zhang, Mare Habermann, Christian Theobalt, et al. Wonder3d: Single image to 3d using cross-domain diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 9970-9980, 2024.
Ziqi Ma, Yisong Yue, and Georgia Gkioxari. Find any part in 3d. arXiv preprint arXiv:2411.135555 2024.
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In international conference on 3D vision, pp. 565-571. Ieee, 2016.
Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas Guibas, and Hao Su. Partnet: A large-scale benehmark for fine-grained and hierarchical part-level 3d object understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 909-918, 2019.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. Transactions on Machine Learning Research Journal, pp. 1-31, 2024.
Maks Ovsjanikov, Mirela Ben-Chen, Justin Solomon, Adrian Butscher, and Leonidas Guibas. Func- tional maps: a flexible representation of maps between shapes. ACM Transactions on Graphics, 31 (4):1-11, 2012.
Songyou Peng, Kyle Genova, Chiyu Jiang, Andrea Tagliasacchi, Mare Pollefeys, Thomas Funkhouser, et al. Openscene: 3d scene understanding with open vocabularies. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pp. 815-824, 2023.
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing Systems, 30, 2017.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pp. 2021.
Nikhila Ravi, Valentin Gabeur, Yuan- Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Segment anything in images and videos. In The Thirteenth International Conference on Learning Representations.
Habib Slim, Xiang Li, Yuchen Li, Mahmoud Ahmed, Mohamed Ayman, Ujjwal Upadhyay, Ahmed AbdeIreheem, Arpit Prajapati, Suhail Pothigara, Peter Wonka, and Mohamed Elhoseiny. 3dcom- pat++: An improved lcrge-scale 3d vision dataset for compositional recognition. IEEE Transactions Pattern Analysis and Machine Intelligence, 47(12): 1431-1141111114455 2025.
12

---

Published as a conference paper at ICLR 2026
Ayca Takmaz, Elisabetta Fedele, Robert Sumner, Mare Pollefeys, Federico Tombari, and Francis Engelmann. Openmask3d: Opea-vocay 3d instance segmentation. Advances in Neural Information Processing Systems, 36:68367-688388 2023.
George Tang, William Zhao, Logan Ford, David Benhaim, and Paul Zhang. Segment any mesh: Zero- shot mesh part segmentation via lifting segment anything 3d. arXiv e-prints, pp. arXiv-2408, 2024.
Hugues Thomas, Charles R Qi, Famanell Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas. Kpconv: Flexible and deformable convolution for point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6411-6420, 2019.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems, 30, 2017.
Yan Wang, Baoxiong Jia, Ziyu Zhu, and Siyuan Huang. Masked point-entity contrast for open- vocabulary 3d scene understanding. In Proceedings the Computer Vision and Pattern Recognition Conference, pp. 14125-14136, 2025.
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph enn for learning on point clouds. ACM Transactions on Graphics, 1-15) 2019a.
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph enn for learning on point clouds. ACM Transactions on Graphics, 38(5):1-15 2019b.
Tong Wu, Jiarui Zhang, Xiao Fu, Yuxin Wang, Jiawei Ren, Liang Pan, Wayne Wu, Lei Yang, Jiaqi Wang, Chen Qian, al. Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation. /EEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 803-814, 2023.
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 21469-21480, 2025.
Jihan Yang, Runyu Ding, Weipeng Deng, Zhe Wang, and Xiaojuan Qi. Regionple: Regional point- language contrastive learning for open-world 3d scene understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19823- 2024a.
Yunhan Yang, Yukun Huang, Yuan-Chen Guo, Liangjun Lu, Xiaoyang Wu, Edmund Y Lam, Yan-Pei Cao, and Xihui Liu. Sampart3d: Segment any part in 3d objects. arXiv preprint arXiv:2411.07184, 2024b.
Yunhan Yang, Yuan-Chen Guo, Yukun Huang, Zi-Xin Zou, Zhipeng Yu, Yangguang Li, Yan- Pei Cao, and Xihui Liu. Holopart: Generative 3d part amodal segmentation. arXiv preprint arXiv:2504.07943 2025.
Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas. scalable active framework for region annotation in 3d shape collections. ACM Transactions on Graphics, 35 2016.
Biao Zhang and Peter Wonka. Point cloud instance segmentation using probabilistic embeddings. In Proceedings of the IEEE/CVE Conference Computer Vision and Puttern Recognition, pp. 8883-8892, 2021.
Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi 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 Graphics, 13(4):1-20, 2024.
13

---

Published as a conference paper at ICLR 2026
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVE International Conference on Computer Vision, pp. 1836-3847, 2023.
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun. Point transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16259-16268, 2021.
Ziming Zhong, Yanyu Xu, Jing Jiale Xu, Zhengxin Li, Chaohui Yu, and Shenghua Gao. Meshseg- menter: Zero-shot mesh semantic segmentation via texture synthesis. In European Conference Computer Vision, pp. 182-199, 2024.
Yuchen Zhou, Jiayuan Gu, Tung Yen Chiang, Fanbo Xiang, and Hao Su. Point-SAM: Promptable 3d segmentation model for point clouds. In The Thirteenth International Conference Learning Representations, 2025.
Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Ziyao Zeng, Zipeng Qin, Shanghang Zhang, and Peng Gao. Pointelip v2: Prompting clip and gpt for powerful 3d open-world learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2023.
14

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 ADDITIONAL DETAILS
A.1.1 DETAILS OF THE SEGMENTATION PIPELINE
Overall. Given an input point cloud Pm E RRxd and set of prompt points Pprompt IR RR-
PartSAM predicts a binary part mask Mout.
Encoder. The encoder contains two triplane branches. Each of them follows a similar architecture to Liu et al. (2025). The learnable branch differs only in its input layer, where six additional feature channels (representing XYZ coordinates and normals) are concatenated to the coordinates. Within each branch, a PVCNN (Liu et al. (201)) operating at voxel resolution of 323 first extracts per per-point features, which are orthogonally projected onto three axis-aligned planes to form the initial triplane field Fplane E x xO" The planes are then downsampled by a factor of r via a two-layer CNN and reshaped into a sequence of size (3HW/r²) x C which is subsequently processed by a transformer. The transformer outputs are upsampled through transposed convolution layer, reshaped back into three planes, and summed, yielding a triplane representation of size 3 x H x W x C. Point-wise features sampled from the resulting planes according their coordinates. A sampling- und-grouping operation (Qi et al. 2017) further aggregates these features to produce the input embeddings F. E RNN.C."
Decoder. For single prompt, we use three output tokens and one IoU token, resulting in three predicted masks. For multiple prompts, we follow SAM (Kirillov al. 2023) by adding an additional output token that predicts a single aggregated mask, which is ignored in the single-prompt case and used exclusively in the multi-prompt setting. Thus, both settings, the decoder always receives five special tokens (three output tokens, one loU token, and one auxiliary output token). Prompt points are encoded by sampling the triplane feature field at their coordinates and adding positional and prompt- type (positive or negative) embeddings, producing F3 E IR-N, xC" We project the input embeddings F. to the same dimension C" via linear layer, yielding F. E N. Output tokens and the IoU token are concatenated with Fp form unified token set with shape of (Np + 5) x C², which is processed jointly with Fa. The two sets interact through four-layer two-way transformer (similar to Kirilllo et al. (2023), ) where each layer applies self-attention within tokens, followed by cross- attention from tokens embeddings and another cross-attention from embeddings back to tokens. This produces refined output token To refined IoU token T... and updated input embeddings F". The refined input embeddings F². are upsampled to per-point resolution via distance-based interpolation followed by an MLP, yielding a tensor of shape N x C². Then, each refined C' didmensiional output token predicts mask logits through a dot product with the upsampled embeddings, which is passed through a sigmoid and then thresholded to obtain a binary mask.
ToU head. When single prompt is given, the refined IoU token TV E RC is passed through three-layer MLP to predict mask-wise loU scores. The mask with the highest score is then selected as the final output Mout-
Hyperparameters. In all experiments, we follow the setup of Liu et al. (0225) and uniformly sample the input point cloud to N, 100,000 points. For shapes without texture, we assign all points a default gray color. Our encoder is configured with triplane resolution of H = W = 512, Crimit 64, Ctrans 1024. C' 448, and transformer layers per branch. The number of point patches N₂ is set to 2, 000. The feature dimension of two-way transformer layers C" is set to 256.
A.1.2 MECHANISM OF INCORPORATING PREVIOUS POINT PROMPT
In the first round of interaction, we input the first prompt point and obtain the initial mask result. In subsequent rounds, we concat the last predicted mask logits with coordinates and adopt the same sampling-ame and grouping strategy as in Equation (1) to extract dense mask embeddings F, S N.x. These dense embeddings are directly added to the input embeddings F., allowing the model to incorporate information from previous prompts. For the new prompt point, we distinguish positive and negative prompts by adding different learnable prompt-type embeddings to Fy.
15

---

Published as a conference paper at ICLR 2026
A.1.3 DATA CURATION DETAILS
Our data curation follows a fully automated two-stage pipeline designed construct large-scale, high-quality 3D part annotations. In the first stage, several safeguards are applied to filter out low- quality data from Objaverse(-XL.. We remove scanned objects that generally exhibit poor geometric fidelity and are unsuitable for part annotation. We further discard parts that are excessively small or large based on their foreground point statistics, and exclude objects containing fewer than 3 or more than 50 parts to maintain reasonable segmentation granularity. In the second stage, we focus exclusively on bigh-poly, artist-designed meshes that provide rich geometric structure and numerous connected components. Artist-defined connectivity information is incorporated into PartFicld's clustering process to produce clean, well-separated segments. model-in-the-loose procedure then imposes two additional safeguards: (i) part-level filter that accepts only segments meeting the interactive IoU criterion, and (ii) a shape-level filter that retains only shapes with more than five valid parts. Only shape-part pairs satisfying all conditions preserved, ensuring that clustered masks lacking clear semantic meaning (e.g., wheels merged into the car body) are consistently removed.
To ensure that the pipeline scales to millions of shape-part pairs, the whole data curation process is designed to be fully automated. Although a very small number of imperfect cases may remain due to the absence of human verification, their proportion negligible and does affect model behavior. As evidenced by the scaling curves in Figure [1] model performance continues to improve dataset size increases, demonstrating both the scalability of the model and the overall high quality of the curated training data.
A.1.4 TRAINING DETAILS
During training, we simulate the interactive segmentation process as follows: For each ground-truth foreground mask, we first sample the prompt point from the foreground region, selecting points whose distance to the background falls within the bottom third of the total distance range. After obtaining the predicted masks, we iteratively sample subsequent prompt points by selecting the furthest points within the error regions (i.e., the areas where the predicted and ground-truth masks differ). This process is repeated 9 times per mask. We set a = 2 and X = 0.5. A comprehensive data augmentation strategy is employed improve model robustness by introducing controlled variations in geometry, appearance, and scale. The augmentation pipeline includes random rotations, scaling, flipping, and center shifting of the point cloud, as well chromatic transformations such auto contrast, translation, and jitter. Overall, PartSAM is trained using the Adam W optimizer for 250k iterations with a batch size of 4 on NVIDIA H20 GPUs. The learning rate is initialized 5 x 10-4 and decayed by a factor of 0.7 every 50k iterations.
A.2 ABLATION STUDY AND DISCUSSIONS
We ablate PartSAM by removing or modifying its key components and evaluate both interactive and automatic segmentation, as reported in Table
Table 3: Ablation study on PartObjaverse-Ting YYng et al. 2024b). We report the mean IoU on instance-level labels for both the interactive and automatic segmentation tasks.
Method
loU@1
loU@3
IoU@5
loU@7
Automatic
Model Variants
Point-SAM
38.9
62.9
71.5
76.8
48.6
w/o pre-trained weights
48.3
73.6
79.1
80.5
60.5
Frozen PartField Learnable PartField
42.5
69.4
73.6
78.3
54.3
50.8
73.9
79.9
83.2
61.8
Training Data Variants
PartNet
33.7
59.
67.3
70.5
40.2
w/o Model-in-the-loop Annotation
49.0
72.3
78.7
81.1
62.6
Ours
56.1
79.0
84.7
86.9
68.5
16

---

Published as a conference paper at ICLR 2026
A.2.1 ABLATION OF MODEL ARCHITECTURE
To assess the impact of the model architecture, we compare three variants of PartSAM trained under identical settings.
Comparison with Point-SAM. To comprehensively compare with the existing promptable segmenta- tion model Doint-SAM (Zou et al. (025). we train it using our curated dataset and report the results in Table The significant performance drops on both the interactive and automatic segmentation tasks indicate that Point-SAM fails to scale on our native 3D data. Compared with Point-SAM, our method offers advantages in both network architecture and training scheme for the part segmentation task. Regarding architecture, Point-SAM uses transformer encoder operating unstructured point clouds, whereas our method performs transformer modeling on more advanced triplane feature field. The dual-branch encoder also brings in 2D SAM priors, which improve generalization and stabilize training on lcage-scale 3D data. We also benefit from the training scheme: the triplet contrastive loss strengthens part-aware feature learning, and its formulation is naturally robust to the scale and granularity ambiguity of 3D parts. Together, the continuous feature representation and the contrastive learning scheme give framework substantially stronger scalability.
Analysis of Pre-trained Encoder. Then, we evaluate a variant that trains PartS entirely from scratch, without using any initialization. This variant performs worse in terms of all tasks, indicating that the SAM--drivved priors provide useful part-aware cues while not introducing lifting-related artifacts. Next, we employ the pre-trained PartField the encoder while keeping it frozen. The suboptimal performance under this setting shows that training only the decoder is insufficient for scaling our dataset, highlighting the importance of the encoder in building a promptable segmen- tation model. We then fine-tune the entire PartField encoder and observe notable improvements; however, its performance remains below that of our dual-branch encoder. This gap mainly stems from catastrophic bogetting-diree fine-tuning erodes the strong priors inherited from SAM, leading to degraded segmentation quality. In contrast, our dual-branch design preserves these 2D SAM priors in frozen branch, while the learnable 3D-native branch acquires fine- grained 3D semantics from large-scale training. This architecture avoids forgetting effects and supports integrating auxiliary inputs (e.g., normals or RGB), resulting in the best overall performance.
A.2.2 ABLATION OF TRAINING DATA
We next examine the effect of training data. Training only on PartNet (Mo et al. 2019) yields poor generaliza- tion, as the closed-world supervision fails to transfer
3D Native Label
2D Lifting Label
open-world sce- narios. Using the curated data from Stage (Sec. 3.5) improves general- ization despite its limited diversity. Fi- nally, augmenting with annotations from our model-in-the-lon provides further gains, confirming that large-scale and diverse supervision is essential for developing scalable part segmentation model.
pipeline
Figure 10: Visual comparison of native 3D part labels with 2D lifting part labels.
Overall, the two ablations re- veal complementary insights: the promptable model design ensures scalability, while diverse training data drives generalization across open-world settings.
A.2.3 ADVANTAGE OF 3D NATIVE DATA AND SCALING ANALYSIS
In Figure 10 we compare our native 3D part labels with the 2D-lifting results produced via community detection in Tang et al. 2024). The lifted 2D masks are frequently incomplete and overly fragmented due to heavy occlusions, making them unsuitable as reliable supervision in framework. In contrast, our native 3D annotations maintain strong spatial coherence and accurately capture interior structures. Notably, even when the outer clothing of character model is removed (second column), our 3D labels
17

---

Published as a conference paper at ICLR 2026
still reveal clean and consistent part segmentation beneath the surface outcome fundamentally unattainable for 2D-lifting methods that only observe the visible exterior. The advantage of precise interior part segmentation is also illustrated in Figure
To further evaluate the effect of large-scale native 3D training, in Figure IT we present the scaling curve of PartSAM, showing how segmentation performance improves with increasing training data size, from 40k to 500k shapes. The results indicate a consistent improvement in both interactive and automatic segmentation, suggesting that PartSAM continues benefit from large-scale 3D-native supervision. Notably, the performance gains persist even at the largest data size, demonstrating that the model is scalable and benefits from large-scale 3D-native supervision.
A.2.4 APPLICATION
Benefiting from the powerful zero-shot ability, PartSAM can be applied in various downstream tasks and significantly outper- forms prior works. Here, we showcase two representative tasks.
Interactive
Automatic
65
Segmentation Accuracy (loU)
Interior Part Editing. PaRtSAM demon- strates strong performance segmenting interior 3D structures, enabling detailed manipulation of segmented parts. Once part is accurately segmented, it can un-
60
55
dergo various types of edits, including ma-
50
terial modifications (e.g., texture color adjustments) and spatial changes, such as repositioning reorienting within the 3D space. These capabilities make P.M particularly useful for applications requir- ing fine- siningrained control over complex 3D models, such as design and manufacturing processes.
45
200
300
400
500
Training Data Size (x1k shapes)
Scaling curve of PartAAM with respect to training data size. The plot shows segmentation accu- racy (IoU for interactive segmentation with 1 prompt point and automatic segmentation) as the training data increases from 40k 500k shapes.
Figure 11:
Amodal Part Segmentation AI- Al-generated meshes are often presented as unified whole, with initial segmentation typically resulting in fragmented parts. Due to Part- SAM's strong generalization capabilities, it can accurately segment complex structures even when they incomplete or fragmented. By integrating PartSAM with the part completion model HoloPart (Yang et al. 2023), these segmented fragments can be seamlessly reconstructed, achieving comprehensive and complete decomposition of the mesh. This combined approach ensures that the segmented parts are not only accurate but also fully realized, overcoming the limitations standard segmentation techniques.
A.2.5 RESULTS ON PARTOBIAVERSE TINY WITH MESH CONNECTIVITY
In the evaluation of automatic segmentation PartObjaverse- Tiny (Section PartField (Liu et al. 2025) and our method in a setting where mesh connectivity is disabled. The rationale behind this choice is further explained through the quantitative results in Table Rather than training PartField, we use a randomly initialized model to generate the feature field and apply agglomerative clustering to produce segmentation results with mesh connectivity (PartField (B) in T)ble Interestingly, the segmentation performance is significantly better than that of the pre-trained PartField when mesh connectivity is disabled (PartField (A)), with 27% improvement.
compare
This result suggests that the mesh connectivity in PartObjaverse Tiny Yangg et al. 2024b) contains rich prior knowledge about part labels, owing to the artistically crafted nature of the dataset. Consequently, using mesh connectivity for clustering in this dataset leaks ground-truit -ruth label information, leading to an unfair evaluation. Moreover, for shapes that require segmentation (such as Al-generated meshes), well-defined mesh connectivity is often absent. Therefore, we disable mesh connectivity in our main experiment to ensure a more representative evaluation.
18

---

Published as a conference paper at ICLR 2026
Material lechtng
Repositioning
(a) Interior Part Editing
(b) Amodal Part Segmentation
Figure 12: Application of PartSAM (a) Empowered by native 3D training data, PaRtSAM accurately segments interior parts and enables material editing and repositioning. (b) Combined with the recent part completion model HoloPart (Yang et al., 2025). PartSAM supports amodal part segmentation, achieving complete decomposition of Al-generated meshes.
Moreover, as indicated by the results in the last row of Table we find that PartSAM can also benefit from mesh connectivity information. After getting the per-face segmentation labels using segment-every part mode. apply graph cuts to refine the segmentation, following Boykov al. (200T). Specifically, construct a mesh graph where each face treated as node, and edges represent adjacency relation- ships between faces. The graph cut algorithm minimizes an energy function that balances two key components: (1) data term which mea- sures the disagreement between the refined label assignment and the initial per-face predictions, and (2) smoothness term that penalizes large label changes between adjacent faces, encouraging spatially coherent boundaries. By minimizing this energy function, this post-processing ensures adjacent faces have consistent labels, thereby improv- ing segmentation accuracy. By using connectivity information this way, PartSAM outperforms PartField under this setting (Ours (B)), further demonstrating the flexibility of method.
Table 4: Quantitative comparison of automatic segmentation on PartObjaverse- Tiny (Yang et al. 2024b) under different settings. We report the mean loU on instance-level labels.
Method
connectivity
Training
loU
PartField (A)
51.5
PartField (B)
65.6
PartField (C)
79.2
Ours (A)
69.5
Ours (B)
81.3
A.2.6 COMPLEXITY ANALYSIS
We present the complexity analysis in Table reporting both the inference time single NVIDIA H20 GPU and the number of network parameters. Compared with lifting- methods that require per-shape optimization (Tang al. 2024, Yang et al. 2024b), PartSAM achieves substantially faster inference due to its fordard design. Compared with PartField (Liu et al., 2025), PartSAM incurs only slight increase in inference time for segmenting an entire shape. Although our encoder is built upon PartField, it eliminates the need for per-face dense feature sampling and clustering, Furthermore, our decoder is lightweight, ensuring that the overall inference time remains well- controlled. In addition, we further report the runtime and performance under different numbers of prompt points (1024/512/256). The results show that PartSAM delivers consistently strong performance across all settings, indicating that our method is robust to the choice of prompt density. These results demonstrate that our method effectively balances computational cost and performance.
A.2.7 CROSS-SHARE CONSISTENCY.
We follow the evaluation protocol of Liu et al. (2025) and use functional maps (Ovsjanikov et al. 2012) to compute correspondences between pairs of shapes in Figure 13 For similar quadruped shapes (first row), both PartField and our PartSAM achieve reasonable cross-shape consistency, yet ours yields clearer correspondences in fine- -nninidd regions such as the tail. When the structural gap becomes larger (second row), our method remains robust- robust-prodee producing correct correspondences on challenging parts like the mand--whereas PartField fails and yields entirely incorrect mappings.
19

---

Published as a conference paper at ICLR 2026
These results highlight the stronger part-aware consistency of our learned features under large-scale native 3D supervision.
A.2.8 FAILURE CASES AND LIMITATIONS.
We present failure cases of PaRtSAM in Figure 14 and provide additional results on diverse meshes in Figures 17 and [ where failure cases are highlighted using red dashed boxes. Although PartSAM benefits from large-scale native 3D supervision, the diversity of existing 3D datasets (Deitke et al. 2023b remains limited compared with high-coverage 2D datasets such SA-1B (Kirillov et al. 2023). Consequently, structures that rarely appear during curation may not be accurately segmented test time. For example, in the first row of Figure 14 PartSAM fails to extract the engraved letters carved into surfaces- these surface-level markings never appear as valid parts in current 3D pipelines. SAMesh can sometimes detect such tiny engraved regions due to its direct use of SAM outputs, though its multi-view masks may be inconsistent and introduce noise. PartField, which relies on feature space clustering, is even less able to recover such fine- grained details. Another type of failure arises when an object lacks a clear semantic structure, exemplified by the coral sculpture in the second row. In these cases, both PartSAM and existing baselines struggle to produce meaningful decompositions because the object itself does not possess a coherent part hierarchy.
These cases highlight a limita- tion of current 3D data curation: the class-agnostic nature of part annotation, combined with the in- herent ambiguity of gpeldworld part definitions, leads to a long- tail distribution where many part types appear only rarely. Such imbalance makes it difficult for the model to distinguish the rare, minority parts. This issue be- comes even more pronounced on Al-generated meshes or coarse real-world scans, where geo- metric irreularities, incomplete structures, or hallucinated details further amplify the ambiguity of part boundaries and exacerbate the difficulty of consistent part decomposition.
PartField
Ours
Figure 13: Point-to-point correspondences obtained by Functional Maps OOsjanikov et al. 2012) using features of PartField (Liu et al. 2025) and our encoder as input.
Meanwhile, although PartSAM achieves state-of-the-art perfor- mance in flaass-gnosticc part seg-
mentation, similar to 2D SAM (Kirillov et al. 2023 Ravi et al.).. it cannot directly produce semantic labels for these masks, which are important for some downstream tasks. One possible future direction to address this limitation is to generate large-scale training datasets consisting of 3D shapes, part annotations, and corresponding semantic labels. This could potentially be achieved by utilizing PartSAM's interactive segmentation capabilities, where users provide feedback on segmented parts, facilitating the assignment of semantic categories.
A.3 EVALUATION ON 3DCOMPAT++
To comprehensively evaluate PartSA,, we compare it with PartField (Liu et 2025) and our abla- tion variant trained without the second-stage native 3D data on recent dataset, 3DCoMPaT++ (Slim) ct al. 2025). Since the part annotations in 3DCoMPaT++ are semantic-level labels, both the baselines and PaRtSAM experience performance drops compared with the results on instance-level datasets. Nevertheless, PartSAM still significantly outperforms PartField and also surpasses our ablation variant trained without the second-stage native 3D supervision, highlighting the importance of large-scale 3D data in achieving strong generalization. This performance gap aligns with the characteristics of the dataset. 3DCoMPaT++ ++ contains extremely fine-grained part annotations, which pose challenges for
20

---

Published as a conference paper at ICLR 2026
Table 5: Complexity analysis. We compare the time of automatic segmentation, the time of interactive segmentation, and the number of trainable network parameters.
Methods
Time
Params
Performance
SAMesh
7min
56.9
SAMPart3D
15min
114M
53.5
PartField
10s
106M
51,5
12s (Encoder: 1.2s, Decoder: 0.01sx 1024 points)
69.5
Ours
9s (768 points)
118M
68.8
7s (512 points)
67.9
4s (256 points)
65.2
clustering- based methods like PartField-ww whose clustering step struggles to reliably separate small or rare parts. In contrast, our promptable decoder naturally produces masks across multiple granularities, enabling the model to recognize and segment diverse structures within unified framework. This allows PaRtSAM to handle the fine-grained decomposition required in 3DCoMPaT++
Input
SAMesh
PartField
Ours
hello
hello o
hell
Figure 14: Failure cases of PartSAM.
A.4 ADDITIONAL VISUALIZATION
Visualization of Annotated Part Labels We visualize the part labels annotated using the Model-in- the-loop pipeline in Figure 13 and observe that our pipeline consistently produces high-quality part labels through the proposed filtering rule. For instance, for the gun in the first row, the first prompt results in an IoU that exceeds the threshold, making it valid annotation. The boat part in the first row has a low IoU after the first iteration, but by the 10th iteration, the IoU surpasses the threshold. making it a valid annotation. In contrast, for the parts in the last row, the IoU never exceeds the threshold in any iteration, so no annotation generated.
Visualization of Segmentation Results Additional segmentation results of PartSAM are shown in Figure [6] where it consistently delivers impressive performance across different kinds of shapes. To provide a more comprehensive picture of PartSAM's behavior, we also present randomly sampled results in Figure 17 and Figure 18, where failure cases are highlighted with red dashed boxes.
21

---

Published as a conference paper at ICLR 2026
Table 6: Quantitative comparison of automatic segmentation 3DCoMPAT++ +++ (Slim et al. The best scores are emphasized in bold. We report the mean IoU.
2025
Category
Method
Coarse
Fine-grained
Avg
PartField
47.3
30.5
38.9
Plane
Ours w/o Model-in-the-loop Annotation
57.7
42.6
50.2
Ours
59.4
47.5
53.5
PartField
40.5
20.7
30.6
Car
Ours w/o Model-in-the-loop Annotation
54.1
33.5
43.8
Ours
57.9
37.7
47.8
PartField
51.9
41.4
46.7
Bag
Ours w/o Model-in-the-logg Annotation
61.2
52.7
57.0
Ours
66.9
55.3
61.1
PartField
48.0
44.7
46.4
Coat Rack
Ours w/o Model-in-the-loop Annotation
58.8
46.3
52.6
Ours
62.5
50.2
56.4
PartField
44.2
38.7
41.5
Toilet
Ours w/o Model-in-the-loop Annotation
58.4
62.2
60.3
Ours
60.3
65.7
63.0
IoU@I: 73.6
loU@1: 89.6
loU@ 1: 56.5
loU@10: 83.1
loU@10: 100
loU@10: 93.7
1oU@1: 13.6
1oU@1:
1oU@I:
1oU@10: 45.1
1oU@10: 65.2
loU@10: 71.7
loU@1: 1: 75.5
lU 10.9
ToU@I: 15.3
1oU@10: 98.9
1oU@10: 97.3
loU@10:
loU@1:
1ou@1: 11.9
TaU@I: 43.4
1ou@10:
1oU@10: 72.8
1oU@10 68.7
Figure 15: Example of part labels generated by PartField (Liu et al. 2025 Masks in green dashed
boxes satisfy our loU-based criteria and are retained as training data, while masks in red dashed boxes filtered by model-in-the-lone strategy.
22

---

Published as a conference paper at ICLR 2026
PartObjaverse- Tiny
Al-generated
Figure 16: Additional visualization of PartSAM's automatic segmentation results on PartOejvverse-
Tiny (Yang et al. 2024b) and Al-generated shapes (Lai et al. 2025).
23

---

Published as a conference paper at ICLR 2026
WiZ
Figure 17: Randomly sampled automatic segmentation results on Al-generated shapes Lai et al.
2025). Failure cases are highlighted with red dashed boxes.
24

---

Published as a conference paper at ICLR 2026
PartOhjaverse-Tiny
Real-world Scans
Figure 18: Randomly sampled automatic segmentation results on Yang et al. 2024b) and rerl-world
scans CChoi et al. 2016 Wu et al. 2023). Failure cases are highlighted with red dashed boxes.
25


Reviews:
[{"review_id": "dGExpCfRSE", "reviewer": "Reviewer_vztR", "summary": "This paper proposes a novel method, PartSAM, for 3D part segmentation. The high-level idea of PartSAM follows that of 2D SAM or PointSAM — it trains a feedforward model that takes a point prompt as input and outputs point masks. Unlike recent 3D part segmentation methods that leverage supervision from 2D SAM, this model is among the first to utilize 3D-native part information embedded in the raw mesh.", "questions": "1. **Data release.**\n   Will you release the data processing scripts and the processed datasets with part annotations to facilitate future research and ensure reproducibility?\n\n2. **Use of connectivity information.**\n   I noticed that the paper mentions the proposed method can leverage connectivity information “by applying graph cuts to the segmentation results for one iteration.” However, this description is still unclear. Could you please provide more details on how graph cuts are integrated into the pipeline?\n\n3. **Hierarchical part decomposition.**\n   Could you elaborate on how hierarchical part decomposition is achieved? Specifically, how are finer-grained parts derived from coarser-level segmentations, and what criteria or supervision signals are used to determine the part hierarchy?\n\n4. **Runtime and efficiency.**\n   Regarding the reported timing results, does the measurement correspond to the inference time for a single-point prompt, or to the runtime of the full automatic segmentation process that samples multiple point prompts? How many point prompts are typically used for automatic segmentation, and how does this number influence the overall runtime and efficiency?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper addresses an important task, and the proposed method is well-motivated.\n\n2. The idea of leveraging scene graphs and “model-in-the-loop annotation” is clever.\n\n3. The paper is generally well written and easy to follow.\n\n4. The proposed method outperforms previous methods.\n\n5. The authors provide insightful ablation studies in the supplementary materials.", "weaknesses": "1. **Method novelty and comparison with existing work.**\n   From a methodological standpoint, the high-level idea of the proposed model and training scheme closely follows that of 2D SAM, and therefore lacks substantial novelty or conceptual breakthrough. Moreover, similar attempts to extend 2D SAM to 3D already exist (e.g., PointSAM). Can the authors train PointSAM on the newly collected 3D dataset? Would it yield better or worse results? What are the specific advantages of the proposed network architecture or training scheme compared to PointSAM or 2D SAM?\n\n2. **Contribution of large-scale 3D native data.**\n   I understand that one of the main contributions of this paper lies in training on a large-scale 3D-native dataset. However, I would like to see a deeper quantitative and qualitative analysis of the data quality and its impact on model performance. For example, what are the concrete benefits of 3D-native data compared to 2D-lifted data? Is it more accurate, or does it offer other advantages such as better generalization or consistency? It would also be helpful if the authors could provide more discussion on whether the current data pipeline can reliably produce high-quality part annotations, and what remaining challenges exist in the data curation process.\n\n3. **Experiments on data scale and quality.**\n   I recommend adding further experiments to investigate the influence of data quantity and quality, such as scaling curves showing performance variations with different training data sizes.\n\n4. **Impact of connectivity information.**\n   I understand that connectivity information may play an important role in part segmentation. The current experiments only report results without considering connectivity information and appear to adapt the baselines accordingly. For a more comprehensive comparison, it would be beneficial to include both setups—*without* connectivity and *with* connectivity. In many scenarios, it is desirable for the model to utilize connectivity cues to produce more accurate and coherent part segmentations. Such comparisons would provide valuable insights into the model’s capability and potential advantages under different conditions.", "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": "8oiGEAbocQ", "reviewer": "Reviewer_zpAD", "summary": "This paper introduces PartSAM, a 3D part segmentation model that avoids lifting 2D masks, supports interactive prompts, and offers a “segment-every-part” mode for full-shape decomposition. Architecturally, it combines a triplane dual-branch encoder (one frozen to preserve SAM-derived 2D priors, one learnable for native 3D) with a prompt-guided decoder, enabling controllable, geometry-aware segmentation including interior structures. A model-in-the-loop pipeline scales supervision to >5M shape–part pairs from large 3D asset repositories, yielding diverse, fine-grained labels. Experiments show decisive SOTA gains over Point-SAM/PartField on PartObjaverse-Tiny and PartNet-E.", "questions": "Plz address the concerns raised in the weakness and ethics concerns sections.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "I like the paper's architecture–data efforts (SAM-like decoder + dual-branch triplane encoder + large 3D supervision pipeline) yields generalizable segmentation that extends to interior parts. Although no novel technical contributions, but I do like the whole pipeline and believe such model-data efforts are good to community if open-sourcing.\n\nThe evaluation is extensive, including interactive/automatic settings, ablations, complexity analysis. The results show consistent over strong baselines, including SAMPart3D and PartField.", "weaknesses": "The part-level are ambitious or the part definition is unclear. For example, the left-second human-horse case in Figure 1 segment the tail into two weird chunks, while the hair and the horse head are not separate. Also, the current multi-scale is generated by varying NMS\nthreshold value, and also the beginning is by using PartField along with different clustering number. I feel this is a fundamental limitations for the current setup.\n\nThe performance of SAMesh is actually quite good as well, where the proposed method is superior compared to SAMPart3D and PartField, as the boundaries is more clear. However, SAMesh also has clear boundaries. The proposed method is more fine-grained in large pieces, while SAMesh tend to segment into large pieces, where I feel can tune the 2D segmentation model fine-grained level to adjust this point. Also, the grenade in SAMesh is very fine-grained. Therefore, I do not believe the proposed method is absolutely better than SAMesh. For this point, I actually want to see more qualitative comparisons between the proposed method and SAMesh with randomly sampling. Not cherry-pick, but randomly sampling can give reader better understanding of the performance differences. Also, there is no need for the proposed method absolutely better than SAMesh as two methods are driven from very different technical backbones. I just encourage the author to conduct the comparisons more systematics, and show the limitations for both the SAMesh and the proposed method. This will not harm the paper at all.", "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": "W0d6H1mK3W", "reviewer": "Reviewer_9Mx7", "summary": "This paper introduces PartSAM, a promptable 3D part segmentation model trained on native 3D data that outperforms state-of-the-art methods on both interactive and automatic part segmentation tasks. The work also contributes a large-scale 3D part dataset comprising 180K shapes created by merging existing part labels into semantically consistent parts, along with 320K shapes annotated using pseudo-labels.", "questions": "1. Given the weakness 1 and 2, would it be possible to evaluate performance on the car and airplane categories (68 meshes in total) from 3DCOMPAT++ [1] and compare the results with PartField at this stage? It would be particularly valuable if the evaluation could be performed on both fine-grained and coarse part levels.\n2. PartField demonstrates emergent cross-shape consistency when exploring feature space. Could you provide both qualitative and quantitative (if possible) results showing whether PartSAM exhibits similar cross-shape consistency, along with comparisons to PartField?\n3. As mentioned in the model-in-the-loop annotation section, PartField suffers from limited controllability. In my experience, it sometimes cannot even segment all four wheels of a bus. Given this limitation, is using PartField outputs as candidate labels truly a reliable choice to obtain semantically meaningful annotations? Are there specific technical safeguards in place to prevent such failures from compromising pseudo-label quality? \n4. Previous 2D-lifting approaches often suffer from limitations such as 3D inconsistency in 2D models. The motivation for a native 3D solution should therefore be to avoid such error modes inherent in 2D models. Given this context, I am curious why it is necessary to retain 2D priors from SAM in the dual-branch encoder design?\n\n[1] 3DCOMPAT++: A comprehensive dataset for 3D object understanding with fine-grained part annotations", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. PartSAM achieves significant quantitative improvements over existing methods on its benchmarks for both interactive and automatic part segmentation tasks.\n2. In the data curation process, the merging criteria that exclude assets failing to form semantically meaningful parts is an excellent filtering strategy. Although automatic part segmentation is performed in a class-agnostic manner, ensuring that segmented parts can be assigned appropriate semantics remains valuable and well-motivated.", "weaknesses": "1. PartObjaverse-Tiny contains only 200 shapes across 8 categories, which is insufficient to convincingly demonstrate model performance. While evaluation on the PartNetE dataset (1,906 shapes) provides additional evidence, evaluating on larger and more diverse benchmarks such as 3DCOMPAT++ [1] would significantly strengthen the claims.\n2. Although the ablation study in the Appendix demonstrates performance improvements brought by the model-in-the-loop annotation, the results are only evaluated on the small PartObjaverse-Tiny dataset. The paper would benefit from more comprehensive analysis demonstrating annotation quality. Given that the data curation process emphasizes semantically meaningful parts, I believe 3DCOMPAT++'s part definitions would be more appropriate for evaluation, as they possess stronger semantic grounding.\n\n[1] 3DCOMPAT++: A comprehensive dataset for 3D object understanding with fine-grained part annotations", "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": "5pafB3I8pf", "reviewer": "Reviewer_faSk", "summary": "The authors propose PartSAM, a model for 3D part segmentation. Specifically, the PartSAM contains two essential components: encoder with initialized weight from previous work that inherits 2D foundation prior, and further train with native 3D data; and scaling the 3D data with model-in-the-loop annotation. Then the scaling of data results in a very powerful model for 3D part segmentation, with great in-the-wild generalization ability, and better performance comparing to previous methods.", "questions": "Please see the weaknesses above. Besides that, some other questions:\n\n1. Will the non-clean meshes, or meshes with broken geometry in the dataset harm the training process? For example many assets in Objaverse are \"not semantically meaningful\" assets with e.g. a flat thin surface. Is there any process to avoid using these data, or these data are causing some negative effect.\n\n2. It'd be good to show more qualitative results on \"inner structure\" of the model's output comparing to baselines, to showcase the advantage of trained with native 3D data.\n\n3. It'd also be good to show some visualization results about the failure case.\n\nIn general I think this is a good paper, with aforementioned questions and weaknesses being answered/addressed, I'll raise my score accordingly.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper has very clear motivation and intuition for each method components. For example, using PointSAM backbone as initialization to adopt \"2D SAM image prior\" is a clever design and naturally fits. \n\nThe authors also conducted extensive experiments to compare with baselines, and also ablations to show the validness of each proposed components.\n\nThe overall presentation of the paper is good and well-structured, with clear transition from motivation, to method design, to experiment part to show the validness of the proposed method.", "weaknesses": "I think the main weakness is that some details of the method / the system is not that clear in the current paper. I'd like authors to add more explanations on these parts:\n\n1. What's the detailed model architecture to generate mask given point prompt, in the architecture of the network and also the shape format of all the input-output here.\n\n2. What's the mechanism of incorporating previous point prompt? Both as a positive point prompt or a negative point prompt.\n\n3. What's the detailed process of the IoU head in the network?\n\nIn general I think the network architecture and process can be elaborated more, to make it more clear to the readers.", "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)