HoloPart: Generative 3D Part Amodal Segmentation
Authors: Yunhan Yang
Yuan-Chen Guo
Yukun Huang
Zi-Xin Zou
Zhipeng Yu
Yangguang Li
Yan-Pei Cao
Xihui Liu
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Generation
3D Segmentation
3D Part
Primary area: generative models

Abstract: 3D part amodal segmentation--decomposing a 3D shape into complete, semantically meaningful parts, even when occluded--is a challenging but crucial task for 3D content creation and understanding. Existing 3D part segmentation methods only identify visible surface patches, limiting their utility. Inspired by 2D amodal segmentation, we introduce this novel task to the 3D domain and propose a practical, two-stage approach, addressing the key challenges of inferring occluded 3D geometry, maintaining global shape consistency, and handling diverse shapes with limited training data. First, we leverage existing 3D part segmentation to obtain initial, incomplete part segments. Second, we introduce HoloPart, a novel diffusion-based model, to complete these segments into full 3D parts. HoloPart utilizes a specialized architecture with local attention to capture fine-grained part geometry and global shape context attention to ensure overall shape consistency. We introduce new benchmarks based on the ABO and PartObjaverse-Tiny datasets and demonstrate that HoloPart significantly outperforms state-of-the-art shape completion methods. By incorporating HoloPart with existing segmentation techniques, we achieve promising results on 3D part amodal segmentation, opening new avenues for applications in geometry editing, animation, and material assignment.

Published as a conference paper at ICLR 2026
HOLOPART: GENERATIVE 3D PART AMODAL SEG-
MENTATION
Yunhan Yang' Zhipeng Yu?
Yuan-Chen Guo?
Yukun Huang'
Zi-Xin Zou
Yangguang Li
Yan-Pei Cao 253
Xihui Liu E8
The University of Hong Kong
2 VAST
3D Part Amudaa Segmentation
Appliaatim
Generalication
(M)
Det Segnemation
Shope Completion
Gomety Ediing
Ediiig
Figure I: Demonstration of the difference between (a) 3D part segmentation and (b) 3D part amodal segmentation. 3D part amodal segmentation decomposes the 3D shape into complete semantic parts rather than broken surface patches, facilitating various downstream applications. In this paper, we propose a solution by performing 3D part shape completion on incomplete part segments.
ABSTRACT
3D part amodal segmentation-decomposinn 3D shape into complete, seman- tically meaningful parts, even when occluded-is is a challenging but crucial task for 3D content creation and understanding. Existing 3D part segmentation meth- ods only identify visible surface patches, limiting their utility. Inspired by 2D amodal segmentation, we introduce this novel task to the 3D domain and propose a practical, two-stage approach, addressing the key challenges of inferring oc- cluded 3D geometry, maintaining global shape consistency, and handling diverse shapes with limited training data. First, we leverage existing 3D part segmenta- tion obtain initial, incomplete part segments. Second, introduce HoloPart, a novel
model, to complete these segments into full 3D parts. HoloPart utilizes a specialized architecture with local attention to capture fine- grained part geometry and global shape context attention to ensure overall shape consistency. We introduce new benchmarks based on the ABO and PartObjaverse- Tiny datasets and demonstrate that HoloPart significantly outperforms state-of- the-art shape completion methods. By incorporating HoloPart with existing seg- mentation techniques, achieve promising results on 3D part amodal segmen- tation, opening new avenues for applications in geometry editing, animation, and material assignment.
INTRODUCTION
3D part segmentation is an active research area. Given a 3D shape represented polygonal mesh or point cloud, 3D part segmentation groups its elements (vertices or points) into semantic parts. This is particularly valuable for shapes produced by photogrammetry or 3D generative models Zhang et al. (2024); Liu et al. (2023b); Hong et al.] (2023): Long et al. (2024); Zhang al. (2023); Poole cct al. 2022). which are often one-piece and difficult to deal with for downstream applications. However, part segmentation has limitations. It produces surface patches rather than "complete parts" of the 3D shape like is shown in figure (a), where the segmented parts are broken. This may suffice for perception tasks but falls short for content creation scenarios where complete part geometry is required for geometry editing, animation, and material assignment. similar challenge has been learned in 2D for many years, through the research area of 2D amodal segmentation. Numerous

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Published as a conference paper at ICLR 2026
previous works Ehsani et al. (2018); ; Kar et al. ) Ke et al. (2021); Ozguroglu et al. (2024) have explored the 2D amodal segmentation task, yet there remains a lack of research for 3D shapes.
To address this, we introduce the task of 3D part amodal segmentation. This task aims to separate a 3D shape into its complete semantic parts, emulating how human artists model complex 3D assets. figure [ (b) shows the expected output of 3D part amodal segmentation, where segmented parts complete. However, extending the concept of amodal segmentation to 3D shapes introduces signifi- cant, non-trivial complexities that cannot be directly addressed by existing 2D or 3D techniques. 3D part amodal segmentation requires: (1) Inferring Occluded Geometry: Accurately reconstructing the 3D geometry of parts that are partially or completely hidden. (2) Maintaining Global Shape Consistency: Ensuring the completed parts are geometrically and semantically consistent with the entire 3D shape. (3) Handling Diverse Shapes and Parts: Generalizing to a wide variety of object categories and part types, while leveraging a limited amount of part-specific training date.
Recognizing the inherent difficulty of end-to-end learning for this task, we propose a practical and effective two-stage approach. The first stage, part segmentation, has been widely studied, and we leverage an existing state-of-the-art method Yang et al. (2024) to obtain initial, incomplete part segmentations (surface patches). The second stage, and the core of our contribution, is 3D part shape completion given segmentation masks. This is the most challenging aspect. requiring us to address the complexities outlined above. Previous 3D shape completion methods Rao et al. (2022); Chu et al. 2024)); Cheng et al. (2023) focus on completing entire objects, often struggling with large missing regions complex part structures. They also do not address the problem of completing individual parts within a larger shape while ensuring consistency with the overall structure.
We introduce HoloPart, a novel diffusion-basel model specifically designed for 3D part shape completion. Given an incomplete part segment, HoloPart doesn't 't just "fill in the hole". It leverages a learned understanding of 3D shape priors to generate a complete and plausible 3D geometry, even for complex parts with significant occlusions. To achieve this, we first utilize the strong 3D generative prior learned from large-scale dataset of general 3D shapes. We then adapt this prior to the part completion task using curated, albeit limited, dataset of part-whole pairs, enabling effective learning despite data scarcity. Motivated by the need to balance local details and global context, HoloPart incorporates two key components: (I) a local attention design that focuses on capturing the fine-gran graineri geometric details of the input part, and (2) a shape context-aware attention mechanism that effectively injects both local and global information to the diffusion model.
To facilitate future research, we propose evaluation benchmarks on the ABO[Collins et al. (022) and PartObjaverse- Tiny Yang et al. (2024) datasets. Extensive experiments demonstrate that HoloPart significantly outperforms existing shape completion approaches. Furthermore, by chaining HoloPart with shellf 3D part segmentation, we achieve superior results on the full 3D part amodal segmentation task.
In summary, we make the following contributions:
We formally introduce the task of 3D part amodal segmentation, which separates 3D shape into multiple semantic parts with complete geometry. This is critical yet unexplored problem in 3D shape understanding, and provide two new benchmarks (based on ABO and PartObjaverse-Ting to facilitate research in this area.
We propose HoloPart, novel diffusion-based model for 3D part shape completion. HoloPart features dual attention mechanism (local attention for fine-grained details and context-aware attention for overall consistency) and leverages a learned 3D generative prior to overcome limitations imposed by scarce training data.
We demonstrate that HoloPart significantly outperforms existing shape completion meth- ods on the challenging part completion subtask and achieves superior results when inte- grated with existing segmentation techniques for the full 3D part amodal segmentation task, showcasing its practical applicability and potential for various downstream applications.
2 RELATED WORK
3D Part Segmentation. 3D Part Segmentation seeks to decompose 3D objects into meaningful, se- mantic parts, long-standing challenge in 31) computer vision. Earlier studies Qi et al. (2017a b Li
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et al. (2018); Zhao al. (2021); Qian et al. (2022) largely focused developing network architec- tures optimized to learn rich 3D representations. These methods generally rely on fully supervised 3D part annotations. Constrained by the lim- ited scale and diversity of available 3D part datasets |o et al. 2019); [Caan et al.
training. which requires extensive,
(2015). these
approaches often face challenges in open-world scenarios. 'To enable open-world 3D part segmen- lation, recent methods Liu et al. (2023a); Umam et al. (2023); Kim & Sung (2024);
Zhong et al. al. (2023) Tang et al. (2024); Thai al. (2024); Xue et al. (2023); Yang
(2024) : Abdelreheem
et al. 2024); et al. (2024) leverage 2D foundation models such as SAM Kirillov et al. (0223),
GLIP et al. (2022a) and CLIP Radford et al. (202I). These approaches first segment 2D render-
ings of 3D objects and then develop methods to project these 2D masks onto 3D surfaces. However, due to occlusions, these methods can only segment the visible surface areas of 3D objects, result- ing in incomplete segmentations that are challenging to directly apply in downstream tasks. In this work, we advance 3D part segmentation by introducing 3D part amodal segmentation, enabling the completion of segmented parts beyond visible surfaces.
3D Shape Completion. 3D shape completion is a post-processing step that restores missing re- gions, primarily focusing whole shape reconstruction. Traditional methods like Laplacian hole filling [Nalen et al.] (2006) and Poisson surface reconstruction Kazhdan et al. (2006) address small gaps and geometric primitives. With the growth of 3D) data, retrieval-based methods (Sung et al. (203)) have been developed to find and retrieve shapes that best match incomplete inputs from a predefined dataset. The rise of generative models such as GANs Goodfellow al. (2020), Autoen- coders Kingma (003)) and Diffusion models Ho et al. (2020) has led to methods like DiffCom- plete Chu et al. 2024) and SC-Diff Galvis al. (2024), which generate diverse and plausible 3D shapes from partial inputs. inputs. These models offer flexibility and creative freedom in shape completion.
[
3D Shape Diffusion. Various strategies have been proposed address the challenges associated with directly training 3D diffusion model for shape generation, primarily due to the lack of a straightforward 3D representation suitable for diffusion. Several studies [ai et al. (017)); /Zhang et al. (2023); Zhao et al. (2024); Zhang et al. (2024) leverage Variational Autoencoders (VAHs) to encode 3D shapes into a latent space, enabling diffusion model to operate on this latent repre- sentation for 3D shape generation. For instance, Shap-E Dai et al. (2017) encodes point cloud and an image of a 3D shape into an implicit latent space using transformer- based VAE, enabling subsequent reconstruction as Neural Radiance Field (NeRF). 3DShapeD employs cross-attention attention mechanisms to encode 3D shapes into latent representations that can be de- coded through neural networks. Michelangelo Zhao et al. (2024) further aligns the 3D shape latent space with the CLIP Radford et al. (2021) feature space, enhancing the correspondence between shapes, text, and images. CLAY Zhang et al. (2024) trains large-scale 3D diffusion model on an extensive dataset, implementing a hicrarchical training approach that achieves remarkable results.
VecSet VeeSet et al. (2023)
3D PART AMODAL SEGMENTATION
We formally introduce the task of 3D part amodal segmentation. Given 3D shape m. the goal is
to decompose m into a set of complete semantic parts, denoted as (P), ........pp Pn., where each p; represents a geometrically and semantically meaningful region of the shape, including any occluded portions. This is in contrast to standard 3D part segmentation, which only identifies visible surface patches. The completed parts should adhere to the following constraints:
1. Completeness: Each p; should represent the entire geometry of the part, even if portions are occluded the input shape m.
2. Geometric Consistency: The geometry of each p; should be plausible and consistent with the visible portions of the part and the overall shape
3. Semantic Consistency: Each p; should correspond to semantically meaningful part (e.g.,
a wheel, a handle).
As discussed in the Introduction, this task presents significant challenges, including inferring oc- cluded geometry, maintaining global shape consistency, and generalizing across diverse shapes and parts, all with limited training data. To address these challenges, we propose two-stage approach:
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Published as a conference paper at ICLR 2026
Contesi-baar Allention
XN
Shape
Ceclod
Segranme Parn
Leca.
Whole Shape wi Segmemntion Mark
Complete
Scilld Part
Atiention
Figure 2: An overview of the HoloPart model design. Given whole 3D shape and corresponding surface segmentation mask, HoloPart encodes these inputs into latent tokens, using context-aware attention to capture global shape context and local attention to capture local part detailed features and position mapping. These tokens are used as conditions and injected into the part diffusion model via Pross-attention respectively. During training, noise is added complete 3D parts, and the model learns to denoise them and recover the original complete part.
1. Part Segmentation: We first obtain initial part segmentation of the input shape This provides us with set of surface patches, each corresponding to a (potentially occluded) semantic segments {81, 82,... 8n
For this stage, leverage SAMPart3D Yang et al. (2024), although our framework is compatible with other 3D part segmentation techniques.
2. Part Completion: This is the core technical contribution of our work. Given an incomplete part segment 5; our goal is to generate the corresponding complete part p.. This requires inferring the missing geometry of the occluded regions while maintaining geometric and semantic consistency. We address this challenge with our HoloPart model, described in the following sections.
The remainder of this section details approach, beginning with the object-level pretraining used to establish a strong 3D generative prior (section 3.1). followed by the key designs of the HoloPart model (section 3.2). and finally the data curation process (section 3.3). The overall pipeline of HoloPart is shown in figure 2
3.1 OBJECT-LEVEL PRETRAINING
Due to scarcity of 3D data with complete part annotations, we first pretrain 3D generative model on large-scale dataset of whole 3D shapes. This pretraining allows us to learn generalizable representation of the 3D shape and capture semantic correspondences between different parts, which is crucial for the subsequent part completion stage. Variational Autoencoder (VAE). We adopt the VAE module design as described in 3DShape2 VecSet Zhang et al. (2023) and CLAY Zhang et al. (2024). This design embeds the input point cloud X Nx3 sampled from complete mesh into set latent vectors using a learnable embedding function combined with cross-attention encoding module:
2 = E = CrossAttn(PosEmb/X.. PosEmbbX)))
where Xo represents subsampled point cloud from X via furthest point sampling, i.c. Xo (X) E M33 The VAE's decoder, composed of several self-attention layers and a cross- attention layer, processes these latent codes along with list of query points in 3D space, to produce the occupancy logits of these positions:
D(2, 9) CrossAAtt(Pos
SelfAtt((2)).
3D Shape Diffusion. Our diffusion denoising network vo is built upon a series of diffusion trans- former (DiT) blocks/Peebles & Xie (203)) /Zhao et al. (2024); Wu et al. (2024), : Zhang et al. (2024); Liet al. (2024). In line with the approach of Rectified Flows (RFs) Liu et al. (2022): Lipman et al. 2002 Albergo & Vanden-Eijnder (2022), our diffusion model is trained in compressed latent space to map samples from the gaussian distribution e ~ (0, I) to the distribution of 3D shapes. The forward process is defined using a linear interpolation between the original shape and noise, represented as:
t)20 te,
(3)
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Published as a conference paper at ICLR 2026
where 0 I t 1000 is the diffusion timestep, 2u represents the original 3D shape, and 2 is progressively noised version of the 3D shape at time t. Our goal is to solve the following flow matching objective:
ExEE(X))tss-N1..) [Ive (ze, 9, - (c -0)1
(4)
where g is the image conditioning feature Wu al. (2024) derived from the rendering of 3D shape during the pretraining stage.
3.2 CONTEXT-AWARE PART COMPLETION
Given a pair consisting of a whole mesh æ and part segment mask 8; on the surface from 3D segmentation models as prompt. aim to leverage the learned understanding of 3D shape priors to generate complete and plausible 3D geometry p!- To preserve local details and capture global context, incorporate two key mechanisms into pretrained model: local attention and shape context-aware attention. The incomplete part first performs cross-attention with the global shape to leam the contextual shape for completion. Next, the incomplete part is normalized [-1, 1] and undergoes cross-attention with subsampled points, enabling the model to learn both local details and the new position. Specifically, the context-aware attention and local attention be expressed as:
Co = =C(So, X)
(5)
= CooosttttiPoeeeemoooSSPPPsttttam
(6) where represents the sampled point cloud on the surface of the incomplete part mesh, and So denotes the subsampled point cloud from S via furthest point sampling. X represents the sampled point cloud on the overall shape. Here, M is a binary mask used to highlight the segmented area on the entire mesh, and ## represents concatenation.
Q C(Sa, S) Cross (ooSE(PS) PosEmb(SS),
We further finetune the shape diffusion model into a part diffusion model by incorporating our designed local and context-aware attention. The part diffusion model is trained in compressed latent space to transform noise e ~ (0, I) into the distribution of 3D part shapes. The objective function for part latent diffusion is defined as follows:
(x)E [1vo(2,, 1, co, (c (c- 20)1
where K represents the sampled point cloud from the complete part meshes. Following Zhao et al. 20244, we apply classifier-free guidance (CFG) by randomly setting the conditional information to 7ero vector randomly. Once the denoising network to is trained, the function / can generate mp by iterative denoising. The resulting latent embedding then decoded into 3D space occupancy and the mesh is extracted from the part region using the marching cubes Lorensen & Cline (1998).
3.3 DATA CURATION
We process data from two 3D datasets: ABO Collins et al.] (2022) and Objaverse Deitke et al. (023). For the ABO dataset, which contains part ground truths, we directly this information to generate whole-part pair data. In contrast, filtering valid part data from Objaverse is challenging due to the absence of part annotations, and the abundance of scanned objects and low-quality models. To address this, we first filter out all scanned objects and select 180k high-quality 3D shapes from the original 800,000 available models. We then develop a of filtering rules to extract 3D objects with a reasonable wise semantic distribution from 3D asset datasets, including Mesh Count Restriction, Connected Component Analysis and Volume Distribution Optimization. Further details are provided in the supplementary.
To train the conditional part diffusion model f, we develop a data creation pipeline to generate whole-part pair datasets. First, all component parts are merged to form the complete 3D mesh. Next, several rays are sampled from different angles to determine the visibility of each face, and any invisible faces are removed. To handle not-watertight meshes, compute the Unsigned Distance Field (UDF) of the 3D mesh and then obtain the processed whole 3D mesh using the marching cubes algorithm. We apply similar process to each individual 3D part to generate the corresponding complete 3D part mesh. Finally, we assign part labels to each face of the whole mesh by finding the nearest part face, which provides surface segment masks {8x).
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Published as a conference paper at ICLR 2026
4 EXPERIMENTS
Outs
P/C
DIC
F/V
wlo C-a
C-a
P/C:
DVC
Qurs
hed
D,093
0.289
0.155
0.261
0,090
table
D
0.968
0.330
0.042
0
airplane
0.267
0.141
0.241
0.887
Chamler
chaur
lamp
0.170
0.121
0.084
0,107
0,344
0.045
0.035
0.036
0.030
0.031
Chander
faucet
0.258
0.295
0.125
0.076
0.097
mean (instance)
0.122
0.087
mesn (instance)
0.278
0.146
0.888
macan (calegoory)
16
0.080
0.035
0.066
0.025
mzan (category)
0.277
0.145
0.255
0.087
bod
0,148
0.266
0.595
0.792
0.833
car
0.247
0.382
0.325
0.545
tabli
0.180
0.218
0.652
0.791
0.838
airplane
0.231
0.405
0,230
0,572
lamp
0.155
loU
0.601
chair
D.156
0.214
0.490
0.215
0.368
0,254
0.531
mean (instance)
D.1.9
0.235
0.565
0.733
0.764
men (instance)
0.245
0.401
0.246
0.558
mean (category)
0.160
0.241
0.580
0.239
0.771
maam (calegury)
0.246
0.399
0.248
0.562
hed
0.864
0.314
0,443
0.40h
0.635
table
0.390
0.758
airplane
F1-Score
chair
lamp
0.262
0.244
0,342
0.374
0.610
0.631
0.769
0.500
0.817
0.789
Fi-Score 1
0.282
0,416
0,13
0,277
0.614
0.673
(instance)
0.259
0.371
0.689
0.816
0.843
masn (instance)
0.312
0.485
0.721
0.641
maan (calegory)
0.360
mzan (category)
0.313
0.484
0,322
0.644
Success
moan (instancs)
03802
0.84
0.96
0398
Success
(instance)
0.835
0.884
0.995
Table 1: 3D part amodal segmentation results of PatchComplete, DifiComplete, Finetune- VAF, Ours (w/o Context-attention), Ours (with Context- attention), on ABO, reported in Chamfer Distance, IoU, F-Score and Success Rate.
Table 2: 3D part amodal segmentation re- sults of PatchComplete, DifiComplete, SD- Fusion and Ours on 3DCoMPaT++ with 2.5D mask input, reported in Chamfer Dis- tance, IoU, F-Score and Success Rate.
4.1 EXPERIMENTAL SETUP
Datasets and Benchmarks. We propose two benchmarks based two 3D shape datasets: ABO Collins et al. (2022) and PartObjavers- Tiny Yang et al. (2024). to evaluate the 3D amodal segmentation task. The ABO dataset contains high-quality 3D models of real-world household ob- jects, covering four categories: bed, table, lamp, and chair, all with detailed part annotations. For training, we use 20,000 parts, and for evaluation, we use 1, 1,000 parts (60 shapes). Objaverse Deitke eet al. (2023) is large-scale 3D dataset comprising over 800,000 3D shapes. PartObjaverse- -iny curated subset of Objaverse, consisting of 3,000 parts (200 shapes) with fine- grainer part an- notations. Using our processogg pipeline, we construct two evaluation datasets. Specifically, we project the ground-truth masks onto the surfaces of the processed monolithic meshes to serve as inputs, and use the complete part annotations as the outputs (targets) for evaluation. We further eval- uate the 3D amodal segmentation task by replacing the ground-truth truth masks with masks produced by SAMPart3D. We further investigate the generalization capabilities of our model. Specifically, we demonstrate that our method can be integrated with arbitrary 3D part surface segmentation models to process "holistic shells" (generated or scanned), yielding complete and consistent parts. More- over, we extend our exploration to the task of 2.5D part completion on the 3DCoMPaT4 ++ Slim et al.) 2025
Li et al. 2022b) dataset.
We compare our methods against state-of-the-ard shape completion models, PatchCom- plete Rao et al.] (2022). DiffComplete Chu et al. (2024) and SDFusion Cheng et all (2023) using our proposed benchmarks. We train all baselines on our processed ABO and Objaverse datasets using the official implementations. To adapt to the data requirements of these models, we generated voxel grids with SDF values from our processed meshes. Additionally, our VAE model also uses 3D encoder-decoder architectures for 3D shape compression and reconstruction. Thus, we directly fine-tune the VAE on our parts dataset for part completion, serving as baseline method.
Baselines.
Metrics. To evaluate the quality of predicted part shape geometry, we use three metrics: Lı Chamfer Distance (CD) Intersection over Union (IoU), and F-Score, comparing the predicted and ground truth part shapes. We sample 500k points on both the predicted and the group truth part meshes to capture detailed geometry information, used for the CD calculation. To compute IoU and F-Score, we generate voxel grids of size 6 with occupancy values based on the sampled points. Since the baseline methods
sometimes unable to reconstruct effective meshes, we calculate CD, IoU, and F-Score only for the successfully reconstructed meshes. Additionally, we report the reconstruction success ratio to quantify the reliability of each method.

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Published as a conference paper at ICLR 2026
Overall Mush
Scumented PatchComplete
DifiCooplete
Figure Qualitative comparison with PatchComplete, DifiComplete and Finetune- Finetune-VAE on the
ABO dataset.
Method
Overall
Human
Animals
Daily
Buildings
Transports
Plants
Food
Electronics
PatchComplete
0.144
0.150
0.165
0.141
0.173
0.147
0.110
0,118
0.147
DifiComplete
0.133
0.130
0.144
0.127
0.145
0.136
0.129
0.128
0.125
SDFusion
0.137
0.135
0.162
0.146
0.162
0.144
0.104
0.105
0.134
Chamfer |
Tiettun-VAAE
0.064
0.064
0.067
0.075
0.064
0.076
0.049
0.04]
0.073
Ours w/o Local
0.057
0.061
0.083
0.051
0.047
0.075
0.045
0.037
0.057
Ours w/o Context
0.055
0.059
0.076
0.044
0.047
0.053
0.042
0.039
0.056
Ours
0.034
0.034
0.042
0.032
0.032
0.029
0.029
0.041
PatchComplete
0.137
0.129
0.147
0.132
0.116
0.129
0.152
0.156
0,138
DiffComplete
0.142
0.149
0.139
0.142
0.124
0.139
0.153
0.134
0.157
SDFusion
0.235
0.214
0.237
0.229
0.202
0.198
0.265
0.294
0.242
loU
Finetune VAE
0.502
0.460
0.464
0.503
0.513
0.468
0.536
0.583
0.490
Ours wfo Local
0.618
0.582
0.574
0.618
0.634
0.591
0.673
0.677
0.594
Ours w/o Context
0.553
0.535
0.518
0.579
0.593
0.553
0.590
0.609
0.538
Ours
0.688
0.675
0.667
0.699
0.714
0.687
0.709
0.710
0.648
PatchComplete
0.232
0.221
0.246
0.224
0.197
0.220
0,254
0,261
0.233
DifiComplete
0.239
0.250
0.235
0.238
0.212
0.234
0.254
0.225
0.262
SDFusion
0.365
0.340
0.368
0.357
0.318
0.316
0.403
0.442
0.374
FI-Score
Finetune-VAE
0.638
0.600
0.613
0.638
0.646
0.596
0.672
0.718
0.623
Ours w/o Local
0.741
0.71.
0.706
0.743
0.750
0.713
0.786
0.796
0.719
Ours w/o Context
0.691
0.679
0.663
0.716
0.722
0.688
0.727
0.743
0.676
Ours
0.801
0.794
0.788
0.809
0.818
0.798
0.817
0.820
0.767
PatchComplete
0.938
0.989
0.976
0.954
0.843
0.932
0.959
0.935
0.947
Success
Dif[Complete
Finetune-VAE
0.997
0.942
1,000
0.992
0.980
0.996
0.996
0.958
0.997
0.851
0.996
0.936
0.959
1,000
1.000
0.935
0.994
0.950
Ours w/o Context
0.998
0.997
0.996
1.000
0.997
0.994
1.000
1.000
0.997
Table 3D part amodal segmentation results on PartObjaverss-Tinn, reported in Chamfer Distance, IoU, F-Score and Success Rate.
4.2
MAIN RESULTS
We compare our method with PatchComplete Rao et al. 2022). DiffComplete Chu et al. 2024 and our fintuned VAE on the ABO dataset. Quantitative results presented in table with qualitative comparisons illustrated in figure 3 When dealing with parts containing large missing areas, PartComplete struggles generate a plausible shape. PatchComplete and DifiComplete often fail to reconstruct small or thin structures, such as the bed sheets or the connections of the lamp in figure g Although the finetuned VAE can reconstruct parts that have substantial visible areas, performs poorly when completing regions with little visibility, such as the bedstead or the interior of the chair, as shown in figure 3 contrast, our method consistently generates high-quality, coherent parts and significantly outperforms the other approaches in both quantitative and qualitative evaluations.
ABO.
PartObjaverse--ing We also compare method with PatchComplete, DiffComplete, and our finetuned VAE on the Battbbaavers--ing dataset. The shapes in the SartObjaverse-Ting dataset complex and diverse, making part completion more challenging. We calculate the Cham-

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Ovenall Mesh
PatchComplete
DiCCComllete
Finetune-VAF
Ours
GT
Segnnented
Figure 4: Qualitative comparison the PartObjaverse-Timg dataset.
Generated Medh
Surface Segments
Complete Parts
Genernied Mesh
Surfice Segments
Complete Parts
P3 P3-AAM
HoloPart
Scanned Mesh
Segments from SAMesh
Complete Parts
Scanned Mesh
Segments from PariFiekd
Camplete Parts
Figure 5: 3D part amodal segmentation on generated objects and scanned objects from OmniOb- ject3D. Our method seamlessly integrates with arbitrary zero-shot 3D part segmentation models. We can generate even precise joint structures, such as the nections shown in the figure.
joints at the robot's con-
Img & Mask
2,50 Mask
Complete Part
Img & Mask
2,5D Mask
Complete Part
Img & Mask
2,50 Mask Complete Part
Figure 6: 3D part amodal segmentation on 3DCoMPaT++ Slim et al.
2025 with 2.5D mask input.
Overall Shage
Part Shapes
Overall Shape
Part Shapes
Overall Shape
Part Shapes
Figure 7: Geometry Super-resolution. By representing a part with the same number of tokens as the achieve geometry super-resolution.
overall object,
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Method
Overall
Human
Animals
Daily
Buildings
Transports
Plants
Food
Electronics
SDFusion
0.264
0,282
0.365
0.323
0.185
0.254
PatphComplete
0.289
0.267
0.258
0.295
0.382
0.314
0.247
0.231
0.291
Chamfer
DiffComplete
0.231
0.197
0.193
0.252
0.307
0.264
0.206
0.198
0.235
Finetune- VAE
0,178
0,138
0.114
0.202
0.279
0.213
0.140
0.141
0.198
Ours
0.134
0.094
0.086
0.155
0.210
0.144
0.109
0.110
0.162
SDFusion
0.169
0.161
0.124
0.234
0.168
PatchComplete
0.086
0.079
0.097
0.079
0.076
0.076
0.105
0.091
0.084
loU 1
DiflComplete
0.102
0.115
0.093
0.073
0.087
0.122
0.109
0.098
Finctune-VA VAE
0.347
0,370
0,406
0.313
0.299
0.277
0.412
0.381
0.320
Ours
0.455
0.508
0.513
0.415
0.360
0.379
0.522
0.529
0.416
SDFuison
0.273
0.263
0.306
0.260
0.208
0.198
0.316
0.364
0.271
PatchComplete
0,149
0,139
0.168
0.138
0.133
0.134
0.179
0.157
0.147
F1-Score
DiffComplete
0.177
0.198
0.162
0.129
0.153
0.206
0.189
0.170
Finetune-VV VAE
0.473
0.507
0.543
0.433
0.417
0.395
0,540
0.513
0.439
Ours
0.570
0.626
0.628
0.529
0.477
0.497
0.627
0.645
0.533
PatchComplete
0.978
0,992
0.998
0.992
0.957
0.975
0,988
1.000
0.966
DifiComplete
0.942
0.992
0,980
0.958
0.851
0.936
0.959
0.935
0.950
Success
Finetune-VAR VAE
1.000
0.992
0.995
1.000
1.000
0.997
Ours
1,000
1.000
1.000
1.000
1.000
1.000
1,000
1.000
1,000
Table 4: 3D part amodal segmentation results on PantObjaverse-Ting using SAMPart3D's segment masks input, reported in Chamfer Distance, IoU, F-Score and Success Rate.
segmenn
(a) Geoomety Editing
(b) Goometry Processing
Part-Aware
Wio Part-Aware
(c) Material Editing
Figure 8: 3D part amodal segmentation is capable of numerous downstream applications, such as Geometry Editing, Geometry Processing, Material Editing and Animation.
fer Distance, IoU, F-Score, and Reconstruction Success rate for each method, with the quantitative comparison shown in table] Our method consistently outperforms the others, even this chal- lenging dataset. As shown in figure 4 our approach effectively completes intricate details, such as the eyeball, strawberry, which the other methods fail to achieve.
3DCoMPaT++. By back- projectnjn a 2D rendered image and its corresponding 2D mask onto the acquire the corresponding 2.51) mask information. Based this, can
3D mesh surface, complete the geometry of the parts visible the image. Leveraging the fine-grained annotations and 2D renderings from the 3DCoMPaT++ dataset Slim al. (2025); Li et al. (2022b), we select four suitable categories-ove car, airplane, faucet, and bed-for evaluation. In figure we visualize the performance of method on visible parts, while table baseline methods.
presents a quantitative comparison with
Zero-shot Generalization, By leveraging pretraining on the large-scale Objaverse dataset and fine- tuning on processed parts data, our model is capable of zero-shot amodal segmentation. To demon- strate the generalization capabilities of our model a challenging zero-shot setting, we present 31> part amodal sementation results generated meshes and the scanned dataset OmniObject3D Wu ct al. (002)). Current generated and scanned objects typically exist as "holistic shells", lacking complete part geometry. Our method addresses this by seamlessly integrating with arbitrary sur- face segmentation models to recover a full set of parts, achieving a structural quality that closely resembles artificial models. For instance, figure 5 demonstrates our method applied generated objects using SAMPart3D Yang et al. (2024) and P3-SAM Ma et al. (2025) as segmentation meth- ods. Notably, model capable of generating the internal connecting structures between parts.

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Furthermore, figure also presents the results scanned objects, leveraging SAMesh 2024) and PartField Liu et al. 2025) as segmentation methods.
Tang et al.
Guidance Seale
Chamfer 4
IoU 1
FI FI-Score f
Success
Method
Chamfer
F1-0.1
F1-0.05 1
S 1.5
0.059
0.590
0.718
0.995
TripoSG 0.120
0.828
0.626
S = 3.5
0.057
0.618
0.741
0.997
Ours
0.114
0.834
0.667
5
0.058
0.614
0.738
0,996
= 7.5
0.089
0.514
0.641
0.997
Table 5: Comparison with TripoSG. By gen- erating complete and coherent parts, our method achieves better performance.
Table 6: Ablation study of different guidance scales.
4.3 ABLATION ANALYSIS
Necessity of Context-Award Attention. The context-aware attention is crucial for completing in- visible areas of parts and ensuring the consistency of generated components. To demonstrate this, replace the context-aware attention block with local-condition block and train the model. The quantitative comparison shown in and table demonstrates the significance of context-aware attention. The qualitative analysis is provided in the supplementary material.
Necessity of Local Attention.
Local attention is crucial for maintaining details and mapping po- sitions. We perform an ablation study on the local attention module and present the quantitative comparison in table 3 highlighting the necessity of our local attention design.
Effect of Guidance Scale. We find that the guidance scale significantly impacts the quality of our generated shapes. We evaluate four different guidance scales (1.5, 3.5, 5, and 7) on the PartObjjverse- dataset, with the results presented table 6 small guidance scale leads to insufficient control, while an excessively large guidance scale results in the failure of shape construction from latent fields. We find scale of 3.5 provides the optimal balance.
4.4 APPLICATION
Our model is capable of completing high-quality parts across variety of 3D shapes, thereby abling numerous downstream applications such as geometry editing, material assignment and animation. We demonstrate the application of geometry editing in Figures [and (a), and material assignment in Figures I and (c). (c). For example, in the case of the car model, we perform 3D part amodal segmentation, then modify the sizes of the front and rear wheels, increase the number of jars, and expand the car's s width in Blender. Afterward, we assign unique textures to each part and enable the wheels and steering wheel to move. The video demo included the supplementary material. These operations would be difficult to achieve with traditional 3D part segmentation techniques. Additionally, we showcase an example
geometry processing application in Figure S (b).
Our model also has the potential for Geometric Super-resolution. By representing part with the same number of tokens as the overall object, we can fully preserve and generate details of part. A comparison with the overall shape, reconstructed using the same number of tokens by VAE, is shown figure)
5 CONCLUSION
This paper introduces 3D part amodal segmentation, a novel task that addresses key limitation 3D content generation. We decompose the problem into subtasks, focusing on 3D part shape completion, and propose diffusion-based approach with local and context-aware attention mech- anisms to ensure coherent part completion. We establish evaluation benchmarks on the ABO and PartObjaverse- Tiny datasets, demonstrating that method significantly outperforms prior shape completion approaches. Our comprehensive evaluations and application demonstrations validate the effectiveness of approach and establish foundation for future research in this emerging field.
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6 ACKNOWLEDGEMENTS
The research work described in this paper was conducted the JC STEM Lab of Autonomous Intelligent Systems funded by The Hong Kong Jockey Club Charities Trust.
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A APPENDIX
A.1 THE USE OF LARGE LANGUAGE MODELS (LLMS)
We use Large Language Models (LLMS) strictly for minor language editing-grammaat and readabil- ity, not for method design experiments. All technical contributions, including the methodology, equations, and results, are solely the work of the authors.
A.2 IMPLEMENTATION DETAILS
The VAE consists of 24 transformer blocks, with 8 blocks functioning as the encoder and the re- maining 16 the decoder. The part diffusion model consists of 10 DiT layers with a hidden size of 2048, and the context-awarg aware attention block consists of 8 self-attention blocks. To balance effec- tiveness with training efficiency, we set the token number for our part diffusion to 512. The latent tokens, encoded by the context-aware attention block, have a dimension of (512, 512), which are integrated into the part diffusion model via cross-attention, We fine-tune the part diffusion model using the ABO Collins et al. (2022) dataset with 4 RTX 4090 GPUs for approximately two days, using the Objaverse Deitke et al. (2023) dataset with A GPUs for around four days.
We set the learning rate to le-4 for both the pretraining and finetuning stages, using the Adam optimizer. During training, as illustrated in figure 2 we sample 20,480 points from the overall shape, which serve as the keys and values, while 512 points sampled from each segmented part to serve as the query. This results in the context latent dimensions being (512, 512). For each point, use the position embedding concatenated with normal value the input feature. After passing through the denoising UNet, we obtain shape latents of dimensions (512, 2048), representing the complete part's shape. Subsequently, we use the 3D spatial points query these shape latents and employ local marching cubes algorithm to reconstruct the complete part mesh. The local bounding box is set to be 1.3 times the size of the segmented part's bounding box to ensure complete mesh extraction.
A.3 DATA CURATION DETAILS
We develop set of filtering rules to extract 3D objects with reasonable part-wise semantic dis- tribution from 3D asset datasets. We ultimately retain 16,000 parts (50000 objects) in Objaverse as training data. The specific rules are as follows:
Mesh Count Restriction: We select only 3D objects with mesh count within a specific range (2 15) to avoid objects that are either too simple or too complex (such as scenes or architectural models). The example data filtered out by this rule is shown in figure II (a).
For each object, render both frontal and side views of all parts and calculate the number of connected components in the 2D images. We then compute the average number of connected components per object, as well as the top three average values. An empirical threshold (85% of the connected component distribution) is used filter out objects with severe fragmentation or excessive floating parts (floaters). The example data filtered out by this rule is shown in figure (b).
Connected Component Analysis:
We analyze the volume distribution among different parts and ensure a balanced composition by removing or merging small floating parts and filtering out objects where single part dominates excessively (e.g., cases where the alpha channel of the rendered image overlaps with the model rendering by up to 90%). The example data filtered out by this rule is shown in figure I1 (c).
Volume Distribution Optimization:
A.4 MORE ABLATION ANALYSIS
Semantic and Instance Part Completion. Traditionally, segmentation definitions fall into two categories: semantic segmentation and instance segmentation. Similarly, we process our 3D parts from the ABO dataset according to these two settings. For example, in the semantic part completion setting, we consider all four chair legs as a single part, whereas in the instance part completion setting, they are treated four separate parts. Our model is capable of handling both settings
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Overall Mesh
Semantic Parts
Instance Parts
Figure 9: Ablation study of semantic and instance part completion.
Surface Segments
Complete Parts
Surface Segments
Complete Parts
Figure 10: Ablation study segmentation mask input with different levels of granularity.
effectively. We train on the mixed dataset and present the completion results for single bed using the same model weight, as shown figure
Necessity of Context-Aware Attention. To emphasize the importance of our proposed context- aware attention block, we provide both quantitative analysis (refer section 4.3) and qualitative comparisons. As shown in figure [2] the absence of context-aware attention results in lack of guidance for completing individual parts, leading to inconsistent and lower-quality completion out- comes.
Qualitative Comparison of Different Guidance Scales. section 4.3, we provide a quantitative analysis of various guidance scales. Additionally, We illustrate the qualitative comparison of dif- ferent guidance scales in figure 13 Our findings indicate that excessively large or small guidance scales adversely impact the final completion results. Through experimentation, identify 3.5 as an optimal value for achieving balanced outcomes.
Learning Rate Setting, During the fine-tuning stage, experiment with weighted learning rate approach, where the parameters of the denoising U-Net are set to 0.1 times that the context-aware attention block. However, observe that this approach results in unstable training and negatively impacts the final outcomes. We present the comparison of generated parts with different learning rate training setting figure
Ambiguity of Segmentation Mask. Our model is robust to different levels of segmentation granu- larity. As shown figure I0] whether the chair's leg and seat are separated or merged during the segmentation stage does not affect the final quality.
A.5 MORE RESULTS OF 3D PART AMODAL SEGMENTATION
In figure 15 we showcase additional examples of 3D part amodal segmentation applied to generated meshes from 3D generation models. Initially, we employ SAMPart3D Yang et al. (2024) to segment the generated meshes, resulting in several surface masks. Subsequently. segmented part, enabling the reconstruction of a consistent overall mesh by merging the completed parts. For instance, demonstrated in figure 15 our model effectively completes intricate com-
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ponents such glasses, hats, and headsets from the generated meshes. This capability supports variety downstream tasks, including geometry editing, geometry processing, and material editing.
A.6 MORE RESULTS ON PARTOBIAVERSE- TINY
We present more qualitative results the PartObjaverse-Ting dataset in Figures 17 and 16 Our method can effectively complete the details of parts and maintain overall consistency, which other methods cannot achieve.
A.7 LIMITATIONS AND FUTURE WORKS
The outcome of HoloPart is influenced by the quality of input surface masks. Unreasonable low- quality masks may lead to incomplete results. Therefore, a better approach moving forward would be method to generate large number of 3D part-aware shapes, which can then be used to train part-aware generation models.
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a
4
06
(b)
c
Figure 11: Examples of data filtered out by rules.
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Published as a conference paper at ICLR 2026
Context-Aware
Figure 12: The absence of context-aware attention leads lack of guidance for completing indi- vidual components, resulting in inconsistent and lower-quality outcomes.
Guidance Seale: 1.5
Guidance Seale: 3.5
Guidance Seale: 5.0
Guidance Seale:
Figure 13: Visualization of generated parts across different guidance scales.
Same Learning Rate
Weighted Lerming Rate
Figure 14: Qualitative comparison of different learning rate settings.
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Published as a conference paper at ICLR 2026
Generated Mesh
Surface Segments
Complete Parts
Merged
Figure 15: More Results of 3D Part Amodal Segmentation.
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Published as a conference paper at ICLR 2026
Overall Mush & Segmented Parts
PatchComplese
DifCComplese
Finetune- VAE
Ours
Figure 16: More qualitative results on the PartObjaverse-TT Tiny dataset.
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Published as a conference paper at ICLR 2026
Overall Mesh Segmented Parts
PanchComplete
DidtComplete
Finetune-VAL VAL
Ours
GI
Figure 17: More qualitative results the PartObjaverse-Ting dataset.
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Reviews:
[{"review_id": "ozK6TZMuEZ", "reviewer": "Reviewer_BTkd", "summary": "This paper introduces 3D part amodal segmentation, a new task aimed at decomposing a 3D shape into its complete, semantically meaningful parts, including portions that are occluded. The authors propose HoloPart, a novel diffusion-based generative model that takes incomplete part segments and completes them by leveraging both local part geometry and global shape context.", "questions": "Please see the weakness part.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The task presented in the paper is well-motivated and practical.\n2. The dual local and context-aware attention mechanisms is a good design for balancing fine-grained part detail with overall shape consistency.\n3. The presented results look good.", "weaknesses": "1. It is unclear how well the model generalizes to novel part compositions not well-represented in the finetuning data, as the completion may be heavily reliant on the part-whole priors learned from the ABO and Objaverse datasets.\n2. The method's handling of semantic ambiguity is not discussed. For instance, if a mask incorrectly bridges two distinct semantic parts (like a chair leg and the seat), the model's behavior is unpredictable.\n3. The generative completion process can introduce geometric hallucinations that deviate from the original shape's implicit structure. For instance, in Figure 13 (the turkey), the completed parts appear to add new geometry not implied by the original surface, and the final merged object shows significant inter-part overlaps.\n4. There is a disconnect between the paper's \"amodal segmentation\" framing and its core \"part completion\" contribution. The method is entirely dependent on the quality of the initial segmentation and lacks any mechanism to refine, correct, or handle the noisy and often semantically incorrect outputs of the first stage, thus not fully addressing the end-to-end segmentation problem. Changing the narrative of the paper would make the contribution more aligned.", "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": "9JqMPpo7bx", "reviewer": "Reviewer_WfB3", "summary": "The paper proposes a 2 step procedure to do 3D amodal segementation. Stage 1:  it uses an off-the-shelf approach to segment out the semantically consistent but visible portions of the occluded object.  Stage 2:  uses a local and global context aware diffusion method to  complete the occluded 3d subobject. Additionally they also propose a data pipeline strategy to curate  3D shapes with labeled semantic sub parts and their full (amodal) geometry for training and evaluation", "questions": "I am curious to know about the compute comparison between the proposed model and baselines, in terms of flops/training time/ number of model parameters.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1) The central idea of completing the occluded 3d sub part with a local and globally conditioned diffusion model is quite appealing\n\n2) The datapipeline strategy to curate paired data is simple and easy to engineer and would be quite useful for future models.\n\n3) Evaluate their model for various settings and compare to various baselines.", "weaknesses": "1) Reliance on segmentation quality of the off-the-shelf model in stage-1. Difficulties would arise due to some type of domain shift, due thin structures or heavy occlusions that prevent the stage 1 model from being able to segment the sub part well.\n\n2) In general two stage training pipelines are a bit clunky as it requires two models to be trained unlike end-to-end trained models.", "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": "aTwBkAlfx9", "reviewer": "Reviewer_LGMD", "summary": "This paper proposes HoloPart, a framework that decomposes a complete 3D object into multiple complete and coherent 3D parts. The pipeline first employs SAMPart3D to obtain semantic mesh segmentation masks, and then feeds each incomplete segmented mesh into the proposed model, which reconstructs the complete 3D part meshes.\n\nThe contributions lie primarily in the architectural design of the proposed model and the construction of a new dataset for training and evaluation. The method is compared against state-of-the-art shape completion approaches — PatchComplete, DiffComplete, and SDFusion — and achieves superior performance across Chamfer Distance, IoU, F1-Score, and Success Rate metrics. Ablation studies further validate the effectiveness of the Context-Aware Attention, Local Attention, and the influence of the Guidance Scale.", "questions": "1. Since the pipeline encodes the 3D mesh via a VAE, performs flow matching in latent space, and then decodes it back, does the reconstructed mesh deviate geometrically from the original input mesh? If so, how do the authors mitigate or correct such deviations?\n\nThings to improve the paper that did not impact the score:\n- Table 2: The Success Rate metric is mentioned in the text but not shown in the table — please clarify where it is reported.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper tackles a valuable and underexplored problem, focusing on the generation of complete 3D parts rather than full-object completion.\n2. The construction of a dedicated dataset for 3D part completion is a useful contribution that can facilitate future research in this direction.", "weaknesses": "1. The approach relies heavily on existing 3D part segmentation techniques, which could limit its robustness when segmentation quality is poor.\n2. The task formulation assumes the input is a complete object mesh, yet the pipeline includes VAE compression and flow matching in latent space. It is unclear whether the reconstructed meshes remain geometrically consistent with the original object after decoding.", "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": "zvWfXDO8tZ", "reviewer": "Reviewer_BYbA", "summary": "HoloPart introduces a novel diffusion-based model for 3D part shape completion and formally introduces the 3D part amodal segmentation task with two benchmarks (ABO and PartObjaverse-Tiny). It outperforms SOTA shape completion methods on these two benchmarks.", "questions": "1. Based on weakness 1, given a rendered image of a mesh having 3D part annotations, could we use the 2.5D geometry of this view as the initialization for part completion (only consider visible parts in this view)?  It is a more challenging part-completion scenario. Could you demonstrate HoloPart's performance on this 2.5D part geometry on a small scale (e.g., 20-50 examples)?\n\n2. It would also be valuable to provide additional qualitative and quantitative results of HoloPart on car and airplane categories (68 meshes in total) in the 3DCompat++ [1] dataset to show HoloPart's generalization ability in outdoor objects and with more semantically meaningful part definitions. 3DCompat++ has rendered images, and it would be easy to extract the 2.5D part geometry.\n\n[1] 3DCOMPAT++: A comprehensive dataset for 3D object understanding with fine-grained part annotations", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The 3D part amodal segmentation task and the ability to infer occluded 3D geometry of less complex geometric structures (i.e., Parts) are important for 3D understanding and can be beneficial for broad applications.\n2. HoloPart outperforms competitors on the benchmarks on ABO and PartObjaverse-Tiny datasets.", "weaknesses": "## Major Weaknesses\n1. In the two-stage pipeline, the 3D part segmentation method is crucial for final performance. SAMPart3D is not a very robust method. The segmented part definitions are not in control and often require additional merging for evaluation. Missing parts or incompatible segmented part definitions will significantly impact performance. So I don't think it is a very good initialization for the benchmark evaluation.\n\n## Minor Weaknesses\n1. Leveraging 3D generative priors indeed overcomes the limitations of scarce training data. However, it will also introduce their failure modes and may still fail on challenging cases for current 3D shape diffusion models.", "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)