Complete Structure Guided Point Cloud Completion via Cluster- and Instance-Level Contrastive Learning
Authors: Yang Chen
Yirun Zhou
WEIZHONG ZHANG
Cheng Jin
Venue: NeurIPS 2025 spotlight ()
Keywords: 3D computer Vision
Point cloud
self-supervised point cloud completion
contrastive Learning
Primary area: applications
TL;DR: We employ contrastive learning to extract complete point cloud structures from partial (incomplete) point clouds for guiding point cloud completion, achieving state-of-the-art (SOTA) results in the field of self-supervised point cloud completion.

Abstract: Point cloud completion, aiming to reconstruct missing part from incomplete point clouds, is a pivotal task in 3D computer vision. Traditional supervised approaches often necessitate complete point clouds for training supervision, which are not readily accessible in real-world applications. Recent studies have attempted to mitigate this dependency by employing self-supervise mechanisms. However, these approaches frequently yield suboptimal results due to the absence of complete structure in the point cloud data during training. To address these issues, in this paper, we propose an effective framework to complete the point cloud under the guidance of self learned complete structure. A key contribution of our work is the development of a novel self-supervised complete structure reconstruction module, which can learn the complete structure explicitly from incomplete point clouds and thus eliminate the reliance on training data from complete point clouds. Additionally, we introduce a contrastive learning approach at both the cluster- and instance-level to extract shape features guided by the complete structure and to capture style features, respectively. This dual-level learning design ensures that the generated point clouds are both shape-completed and detail-preserving. Extensive experiments on both synthetic and real-world datasets demonstrate that our approach significantly outperforms state-of-the-art self-supervised methods.

Complete Structure Guided Point Cloud Completion via Cluster- and Instance-Level Contrastive Learning
Yang Chen', Yirun Zhou', Weizhong Zhang?.3, Cheng J.n 1.++
Collegg of Computer Science and Artificial Intelligence
School of Data Science, Fudan University
SShangaai Key Laboratory of Intelligent Information Processing [chen_yang23, yrwhou22h Qm f fudan. edu...dd..cc en, (weizhongzhang, j.) @dunnn.. edu.
Abstract
Point cloud completion, aiming reconstruct missing part from incomplete point clouds, is pivotal task in 3D computer vision. Traditional supervised approaches often necessitate complete point clouds for training supervision, which not readily accessible in real-world applications. Recent studies have attempted to mitigate this dependency employing self-supervise mechanisms. However, these approaches frequently yield suboptimal results due to the absence of complete structure in the point cloud data during training. To address these issues, in this paper, we propose an effective framework to complete the point cloud under the guidance of self learned complete structure. A key contribution of our work is the development of a novel self-supervised complete structure reconstruction module, which can learn the complete structure explicitly from incomplete point clouds and thus eliminate the reliance on training data from complete point clouds. Additionally, we introduce contrastive learning approach at both the cluster- and instance-level to extract shape features guided by the complete structure and capture style features, respectively. This dual-level learning design ensures that the generated point clouds are both shape-complete Extensive experiments on both synthetic and real-world datasets demonstrate that our approach significantly outperforms state-of-the-art self-supervised methods.
and detail-pres preeeerviee.
Introduction
The advancement and widespread adoption of 3D sensors, particularly LiDAR, have led to the emergence of point clouds as a dominant representation primarily owning to their ease of acquisition and comprehensive geometric features. However, in real-world scenarios, raw point clouds often suffer from incompleteness caused by factors such as self-occlusion and lighting conditions, which can hinder the performance of downstream tasks, including object detection [2 and segmentation 4 Consequently, point cloud completion has been proposed effective solution to infer complete point clouds from incomplete inputs.
shapes across various domains [0]
Supervised learning is the paradigm adopted by most existing methods [38 typically utilize paired complete and incomplete point cloud data to train neural networks, learning a one-to-one mapping from incomplete point clouds their corresponding complete counterparts. Impressive results have been reported in recent studies. However, since high-quality complete point clouds are often difficult to obtain through real-world scanning, paired data for training frequently sourced from virtual datasets, such as ShapeNet [2]. As result, supervised methods contend with the domain gap when applied to real-world data.
27 These approaches
Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Guided
Completion Networkk
Figure 1: Illustration of ideas. Our method clusters incomplete point clouds using contrastive learning. By aggregating point clouds in the clusters, obtain the complete structure in self-supervised manner, which are used to guide the completion of the point cloud.
To address the above issue, unpaired point cloud completion methods 39 40 have been proposed as potential solution. These methods eliminate the need for paired data, yet they still rely on the complete point clouds from virtual datasets, and thus can not fundamentally resolve the problem. Subsequently, weakly supervised methods [13] [26] were introduced, aiming to leverage the con- sistency of incomplete point clouds from multiple views. However, their effectiveness is highly dependent registration accuracy, and collecting point clouds from different viewpoints poses significant challenges real-world scenarios. Recently, several studies have sought to address this issue using self-supervised paradigm [1. with P2C approach 77] achieving breakthrough results. Nevertheless, due to the absence of guidance from complete structures, the completed point clouds generated by these methods remain suboptimal.
In this paper, we propose a novel self-supervised point cloud completion framework, termed Complete Structure Guided Point Cloud Completion (CSG-PCC). Our method follows an pipeline, while the core innovation lies in adding an effective Complete Structure Reconstruction Module between the encoder and decoder. This module consists of two key components: 1) a feature disentanglement module and 2) a prototype projection module. For feature disentanglement module, we disentangle the point cloud features into two distinct dimensions, i.e., shape and style. The shape feature captures the global structure while the style feature extracts the local details. the prototype projection module, aggregate complete structures from the disentangled shape features to form shape prototypes, which are then used to guide point cloud completion by projected the shape feature to prototypes.
To ensure effective feature disentanglement critical for structure aggregation, we propose a Feature Permutation Consistency Constraint (FPCC). Within FPCC, we randomly swap point clouds' skeleton points obtained through farthest point sampling (FPS) and recompute their k-nearest neighbors to generate recombined point clouds. The FPCC enforces consistency between original and perturbed point clouds both shape and style feature spaces, effectively decoupling the shape and style features. Building upon this disentanglement module, we construct two-branch network architecture and design a dual-level contrastive learning method. Specifically, we employ cluster-level contrastive learning to cluster the shape features of the incomplete point clouds. By aggregating similar shapes each cluster, we learn a corresponding complete structure, as shown Figure Additionally, we use instance-level contrastive learning the results of the two branches to ensure that the learned style features focus on restance-specifi details.
We conducted experiments with CSG-PCC on both real real-world and synthetic datasets to validate effectiveness of our design. Experimental results demonstrate that the dual-level contrastive learning design enables our method to generate point clouds that are both shape-completed and detail- preserving. achieving sfate-of-the-ar performance the self-supervised point cloud completion domain. Our main contributions can be summarized as:

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1. We propose novel self-supervised point cloud completion method, CSG-PCC, which can explicitly extract complete structures guide the completion process.
2. We propose a dual-level contrastive learning framework to enable efficient training of our self--upprvised point cloud completion network.
3. Extensive experiments on both synthetic and real- world datasets demonstrate that our method can significantly outperform state-of-the-an methods.
2 Related Work
2.1 Supervised and Unpaired Point Cloud Completion
Traditional point cloud completion methods can be broadly classified into those that leverage geo-
metric priors 30 [9] and those that utilize template matching techniques [23] 28] With the rise of deep learning, several methods have drawn inspiration from 2D image inpainting techniques and applied them 3D point cloud completion. These methods p 14
typically use 3D convolutional networks to process voxelized point clouds. However, as the resolution of voxel grids increases, the computational cost grows significantly. The introduction of PointNet 24 which directly processes raw point clouds without the need for voxelization or other transformations, marked a significant shift in the field. Then deep learning methods [8 29 27 have achieved significant success in the field of point cloud completion. Despite these advancements, the dependence of supervised point cloud completion methods on fully-complete point clouds remains significant limitation, hindering their application in real-world scenarios. To reduce the dependency on paired data, unpaired methods [1 (3 have been proposed. However, these methods still rely on complete point cloud data, limiting their applicability in real-world scenarios.
2.2 Weakly-Supervised and Self-Supervised point cloud Completion
In contrast to previous methods, approaches 13 21 have proposed weakly supervised paradigms to address the data dependency complete point clouds. [13 utilizes incomplete point clouds from multiple viewpoints to predict the complete point cloud, using geometric consistency across different views constraint. However, these methods require point clouds to be captured from multiple viewpoints, which is not always feasible in real-world scenarios. Later, ACL-SPC [17]. sule--sper method, introduced the concept of adaptive cycle system. Inspired by [15] P2C ( apply mask-reconttruction self-supervised paradigm to the point cloud completion task. The self-supervised methods mitigate the dependency of complete point clouds. However, they didn't fully exploit the structural information corresponding to the complete structure, which limited the performance of completion results. Though [32 adopts self-supervised approach, it requires additional depth images. (20) utilizes an alternative 3D representation, but our research focus on point cloud.
2.3 Contrastive Learning
Contrastive learning has become a powerful paradigm in unsupervised and self-supervised learning tasks, particularly in the domain of representation learning. Early work in contrastive learning 3 16 focused on learning effective representations by contrasting positive and negative pairs of samples in latent space. Later, contrastive learning methods [2] further advanced the field by eliminating the need for negative samples. Contrastive learning has also been explored for point cloud 36). In this work, we propose to introduce contrastive learning into the self-supervised point cloud completion task.
3 Method
For point cloud completion, achieving a complete structure is critical for helping the missing region prediction. However, in self-supervised point cloud completion, the lack of complete point clouds makes it challenging to obtain the necessary structural information. In this paper, we propose novel self-supervised point cloud completion framework, CSG-PCC, which leverages self-learned complete structures to guide the completion process. The overall pipeline of our network is illustrated
3

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Copy [M]] times
coopletion
fanae
Complete Strocture Recosstrction Module
Stags
Encoday-
Terapa
Eshupe
Deooderie
p())
f
Parching & Mark
Network
Frepte
Tinspe
incopplate cloud
Shara Winghts
Canasenate
Sinikeit Calculation
Network
Eement wise Multphication
Pull Cooee
P
Push Aweay
Loss Iter
yompletion
Figure The Pipeline of CSG-PCC. Given partial point cloud Pp. we divide it into patches, followed by random masking to create two incomplete point clouds, p!1) and p!2) Then point clouds are processed through the encoder to extract features, and then fed into the Complete Structure Reconstruction Module. The CSRM are composed of two core components: a) Feature Disentan- glement Module: maps encoder outputs into shape features Sshupe and style features sayle via two disentanglers. b) Prototype Projection Module: Refines fahage via learnable prototype memory bank M, producing crructure-ehhanee
features fstupe- Then we concatenates fahape and fayle as decoder input to generate the completed point clouds. Dual-level contrastive learning are used to ensure structural completeness and detail preservation.
in Figure 2 In the following, we will present our method by introducing the framework especially the complete structure reconstruction module, cluster- and instance-level learning schemes, and the overall optimization in order.
3.1 Framework
As shown in the Figure 2 our contrastive learning- framework maintains two identical branches during training, with each branch following the Mask, we obtain distinct inputs for the two branches, with specific implementation details as follows:
pipeline. Through the Patching &
Patching Mask. Let P. x3 be an input partial point cloud. We first divide the point cloud into K patches using farthest point sampling (FPS) [25]. where k-nearest neighbors are gathered around each sampled center. Then We randomly select K, observable patches from the total K patches, then discard the remaining K K, patches through masking to generate the first incomplete observable point cloud P! E N.33 for Branch Subsequently, we repeat the masking procedure with different set of K - Ko patches removed, thereby producing the second incomplete observable point cloud 2x RRxxx3 for Branch Both processed point clouds serve parallel inputs for network training.
Taking Branch 1 as an exemplar, the encoder first extracts a point cloud feature f E RRP from input P! formally expressed as E(P!")). Our key innovation lies the proposed Complete Structure Reconstruction Module (CSRM) strategically positioned between the encoder and decoder. The CSRM comprises two coordinated submodules:
1) Feature Disentanglement Module: : Composed of two parallel disentanglers, this module decou- ples If into shape feature Sshupe E D/2 and fayle E D/2
2) Prototype Projection Module (PPM): Enhances structural completeness through geometric priors: fahppe PP (Jshape)
The refined shape feature fshupe is then concatenated with fayle and fed to the decoder D generate the completed point cloud pC)) = D([fatape: faytel). Symmetrically, Branch 2 processes (22) through identical modules to produce p! Implementation specifies of Feature Disentanglement Module and PPM are delineated as follows:

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FPS
Shape Disentungler
Shape Feature Consistency
(())
(1) shape
P
P recombined
Style Disentangler
Style Feature
k-NN
Consistency
(2) p
p(2)
style
Figure 3: Illustration of Feature Permutation Consistency Constraint (FPCC). (a) Structural points p(l) shape are extracted from input cloud p( via farthest point sampling (FPS). (b) The k-nearest neighbors from 2b are aggregated as style points p(2) style (c) Recombined point cloud Precomiieed is 'saaee and style which preserves the global structure of Pa and local details of ((2) (d) FPCC enforces feature consistency between P Precombined and original clouds for effective feature disentanglement.
generated by concatenating p(1)
Feature Disentanglement Module. Within the feature disentanglement module, we decompose point cloud features into shape features representing global structures and style features capturing local details. We can effectively aggregate complete structures through cluster-level contrastive learning to the shape features avoiding the interference from local detail variations. And the disentangled style feature can be alse used as to preserve instance-spece specific details details of input. To ensure the disentangled features correspond accurately to global structure and local details, respectively, propose Feature Permutation Consistency Constraint (FPCC), as illustrated in Figure 3 Specifically, we randomly select two incomplete point clouds, D and p(2). Then, we perform FPS on pf to obtain its structural points, shupe For each point in p(1) shape" we find k-nearest neighbors in p(2), forming syye'' By concatenating Shape and style" construct the recombined point cloud P Precombined- We assume that Precombined contains the global structure of p(!) and the local details of p\ so we require their corresponding features to remain consistent, which is formulated as:
Lipee Φ ( Shape
prepmmiined) + & [ shupe
frecembined
Jstyle
where Shapee shape
(()
frecombined are the features extracted from the corresponding point presomhined ((() and fayle
clouds through the network, and ((.) is the Huber loss function (18). Through FPCC, effectively disentangle the shape and style features of the point cloud.
Prototype Projection Module (PPM.. Specifically, in the PPM, we maintain learnable prototype memory bank M. With training (see Section 3.2 for details), each item M2 E D/2 is considered as prototype representing to a unique complete structure. For the input shape feature fahape we compute its cosine similarity with each prototype and apply softmax function to obtain a weight matrix W. The computation formula is as follows:
exp (cos( Mi/l)
JELMI exp (fas(es Mÿ/t)²
where is a temperature scaling parameter. Then, based on the weight matrix W. we project faupe into the prototypes to obtain the refined feature fahapes i.c., fahape EiELm wi M,
5

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3.2 Cluster-level Contrastive Learning
We employ cluster-level contrastive learning to perform clustering on shape features and utilize the learned cluster centers as complete structural information to guide point cloud completion. Specifically, we maintain learnable prototype memory bank M, where each item M, E 2 represents the cluster center with aggregated complete structure and is random initialized. We then define a mapping function v/, which maps point cloud Pp to its nearest shape prototype M;, i.e., (Pp)) = M,. where arg max cos Datupe (PPp) (M),,,,))
We propose an aggregation loss Lagg to learning shape prototypes. The implementation pipeline contains three key phases: 1) The network first produces the completed point cloud P. from the partial input Pp. 2) Then, we concatenate the shape prototype M; with a zero vector and input it into the decoder to generate the complete structural point cloud P.M., 3) We calculate Chamfer Distance (CD) between the cluster-specific structural point cloud PM: and predicted complete point cloud Po. which is formulated in Eq
Lagg = CD PcM.),
where CD takes both directions into account and can be defined through Unidirectional Chamfer Distance(UCDD as CD(P1, P2) UCD(PI, P2) UCD((P, P1). The UCD is commonly used to measure the distance between two point clouds [8]. UCD between two point sets S₁ and S2 is defined as follows:
UCD(S
Σ #ES
min || 1r--8 11122-
|S||
yES2
In addition, we introduce an InfoNCE loss 16 to ensure that the learned shape prototypes maintain sufficient discriminability for preventing prototype collapse. This loss encourages the shape feature of the point cloud to be as close as possible to the mapped prototype, while keeping far away from others. The InfoNCE loss is formulated as:
exp (cos(Jshape (b (P))))/t)
Linto - loog
(5)
M. exp (cos( (fahape Mt))/t)
where Sahape = Dahape (E(Pp). Thus, the final loss for the cluster-level contrastive learning is defined below:
La
Lage + Linfo-
(6)
3.3 Instance-level Contrastive Learning
We propose using instance-level contrastive learning to ensure that the extracted style features focus on the details of the input point cloud. Specifically, shown in Figure 2, two branches employed during training, where the inputs to the branches, () and (!2) are obtained by applying different masks to the same sample. p! and px(²) serve positive samples in the context of contrastive learning. By enforcing the two branches to produce identical completed point clouds, the style
features are guided to capture the unique details of the input. The instance-level contrastive learning
loss is formulated as follows:
Linstance
3.4 Optimization
Finally, we introduce a completion loss for point cloud completion. By computing the Region- Aware Chamfer Distance (RCD) 7 between Pp and the completed point clouds p( and ???) the network is encouraged to reconstruct the visible regions while allowing the completion of unseen regions. The formulation as follows:
Leompletion = RCD(P,, P!)) RCD(P,, P!?)).
(8)
where RCD is aware of observed and unseen regions and thus only evaluates point distance for observed regions, we refer the reader to 7 for more details about RCD. Every input mapped to shape prototype Mi participates in the aggregation loss computation, thereby enabling M, to aggregate the

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Table 1: Quantitative comparison result of our method and other methods the 3D-EPN dataset
using CD-&2 & (x10*). Bold numbers indicate the best performance self-sspe methods.
Method
Supervision
Air
Cab
Car
Cha
Lam
Sof
Tab
Wat
Avg
PCN 38
2.5
8.0
4.8
9.0
12.2
8.1
8.9
6.0
7.4
TopNet 20
2.3
7.5
4.6
7.6
8.9
7.3
7.5
5.2
6.4
Supervised
Poin'TT 37
1.2
6.5
4.0
5.1
4.5
5.4
5.4
2.6
4.3
CRA-PCN
27
0.9
5.9
3.3
4.2
3.9
5.5
3.6
2.3
3.7
C4C
12.6
14.6
18.2
26.2
22.5
8.7
14.3
Unpaired
Inv 39
4.3
20.7
11.9
20.6
25.9
54.8
38.0
12.8
23.6
Gu al.
5.9
20.8
20.4
34.9
26.0
26.0
21.3
Weakly-supervised
PPNet 21
5.6
46.6
22.4
24.3
46.1
36.4
28.4
15.0
28.1
ACL-SPC 17
14.6
25.3
16.4
45.0
60.1
35.6
40.8
29.2
31.6
P2C 7
Self-supervised
4.3
19.4
8.6
13.5
16.3
20.2
18.1
12.0
14.1
CSG-PCC(OUrs)
3.5
16.1
8.5
12.1
14.6
16.4
17.4
9.1
12.2
structural information from these partial point clouds. This can be interpreted guiding the learning of shape prototypes via positive sample in contrastive learning.
Finally, we sum the previously introduced loss terms to obtain the total loss used for network training:
Laverall Acompletion commpletion Commpeeio + Cct + Ainstance iinstance + Xipcehippe;
(9)
where Acompletions Adt Ainstance and Aipce are weighting parameters.
4 Experiments
4.1 Dataset and Evaluation Metrics
To conduct a comprehensive comparison, we performed experiments on both synthetic and real-world datasets. We conducted experiments the synthetic datasets 3D-EPN [9] and PCN 38 Both of them are derived from the ShapeNet (2) dataset, with the former containing more data, e.g., the chair class has 40,000 pairs for training in 3D-EPN while 5,750 pairs in PCN. Moreover, we evaluate our method on real-world dataset ScanNet [8] We employ the ScanNet dataset processed by [5] where both object orientations and positions have been aligned with ShapeNet.
Datasets.
Evaluation Metric. We use & Chamfer Distance (CD)as the evaluation metric for synthetic datasets. In the of real-world datasets, where ground-truth complete shapes unavailable, adopt the Unidirectional Chamfer Distance (UCD), Unidirectional Hausdorff Distance (UHD), and Region-Awee Aware Chamfer Distance (RCD) proposed in P2C 7 evaluate the fidelity of completed point clouds.
4.2 Evaluation on Synthetic Datasets
compare our proposed method CSG-PCC with state-of-the-art self-supervised method and classical supervised, unpaired methods on 3D-EPN datasets. As quantitatively demonstrated in Table I our method establishes new performance records across all eight categories when compared to existing self-supervised approaches. Furthermore, shown in in Table 2 our methods achieve more improvements on PCN dataset. This occurs because the PCN dataset contains less training data and more severe incompleteness (lower average points in input clouds), while our method explicitly extracts complete structural information to better handle challenging scenarios. Although fully supervised methods still show numerical advantages by heavily exploiting complete and paired ground-trun truth data, our method has further reduced the performance gap.
Figure 4 presents qualitative comparison on the 3D-EPN dataset. It can be observed that our method successfully completes the incomplete point clouds with guidance of complete structures. In particular, compared with P2C, method achieves more complete results and sofa categories

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CSG-PCC
Ground
Input
CRA-PCN
ACL-SPC
(Ours)
Truth
Figure 4: Qualitative comparisons on the 3D-EPN dataset demonstrate method achieves both structural completeness (e. (e.g..car, sofa) and detail preservation (e.g., chair, table). The dashed line categorizes the methods based on whether they utilize complete point clouds for supervision.
Table 2: Quantitative comparison result of method and other methods the PCN dataset using CD-62 (x10*). Bold numbers indicate the best performance in self-supervised methods.
Method
Supervision
Air
Cab
Car
Cha
Lam
Sof
Tab
Wat
Avg
PCN 38
3.0
7.5
5.7
9.7
9.2
9.5
9.2
6.2
7.5
TopNet 29
Supervised
2.3
8.2
4.7
8.6
11.0
9.3
7.5
5.2
6.4
CRA-PCN 27
1.1
6.3
3.8
4.3
3.4
5.9
3.7
2.8
3.9
C4C [31
4.1
14.2
9.9
14.6
19.2
27.8
8.4
7.4
14.4
Unpaired
Inv 339
3.9
17.4
11.0
13.8
14.2
23.0
9.7
6.7
14.1
ACL-SPC 17
10.6
38.8
30.5
41.4
63.5
33.2
42.8
29.2
39.2
P2C 7
Self-supervised
4.8
32.3
17.8
18.1
18.6
33.0
19.8
13.7
19.8
CSG-PCC(Ours)
3.6
18.2
15.7
13.7
15.8
28.6
19.3
12.4
15.9
with severe incompleteness, while preserving richer details for structurally complex lamp and chair categories. We also present the qualitative comparison results on PCN data in section A.2
4.3 Evaluation on Real-world Datasets
compare our method with other approaches on real-world datasets, where our method achieves the best performance among sele-supe methods. Both PCN (38) and Inv (39 are pre-trained on the ShapeNet dataset. Inv's superior UCD metric performance over ours stems from its optimization-
based nature, which explicitly employs UCD as the loss function during inference phase. However, visualization results Figure reveal that our method produces outputs that are both structurally complete and more faithful to the input.
4.4 Ablation Study
Model Design Analysis. To examine the effectiveness of our design, we conduct detailed ablation experiments the cabinet, chair, lamp, and sofa categories of the PCN dataset, with results summa-

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Table 3: Quantitative comparison result of our method and other methods on the ScanNet dataset using UCD-&2 4 (x10*), UHDL (x10²), RCD, (x10²). Bold numbers indicate the best performance in self-supervised methods.
Method
UCD
UHD
RCD
category
chair
table
chair
table
chair
table
PCN
38
4.9
6.2
2.1
1.8
39
2.8
9.9
12.2
0.6
0.8
ACL-SPC 17
5.7
6.8
7.0
7.4
2.3
3.1
P2C 7
3.7
6.5
6.7
1.3
CSG-PCC(OOrs)
3.3
3.7
6.1
6.4
0.8
0.9
Table 4: Ablation study on the PCN dataset. Results reported in CD-62 4 scaled by 10a
Model
FPCC
Lelusser
Linstunce
Cab
Cha
Lam
Sof
A
32.3
18.1
18.6
33.0
B
29.6
17.9
17.8
32.2
C
V
19.3
14.8
16.3
29.4
D
V
d
18.2
13.7
15.8
28.6
Input
PCN
Inv
ACL-SPC
P2C
CSG-PCC
(Ours)
Figure 5: Qualitative comparisons on the ScanNet dataset. The dashed line categorizes the methods based on whether they utilize complete point clouds for supervision.
rized in Table The baseline model (Model A) adopts the P2C architecture. We then modify the network by adding shape-style disentanglers and computing FPCC for feature decoupling, forming Model B. Through shape and style feature disentanglement, the network acquires awareness of global structures and local details, achieving moderate improvements across all four categories. Subsequent integration of the prototype projection module with cluster-level contrastive learning constitutes Model C, enabling explicit extraction of complete point cloud structures from shape features. Guided by this complete structural information, our method demonstrates significant performance gains, particularly benefiting severely incomplete categories like cabinets and sofas. Finally, we incorporate instance-level contrastive learning to establish our complete CSG-PCC framework (Model D), achiev- ing state-of-the-arr results. Due to space constraints, we include additional ablation experiments in the Appendix section, covering hyperparameter selection, prototype visualization, and other analyses.

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Limitations
Although CSG-PCC has demonstrated promising results in completing point clouds with only single partial data needed for learning, several limitations still need to be addressed. Notably, all structural information in our method is derived solely from the training data. If the incomplete point clouds in the training set inherentl lack certain structures (e.g., all aircraft samples missing wings), our approach cannot recover such missing components (e.g., wings), This limitation aligns with existing self- methods.
6 Conclusion
In this paper, we propose a noval self-supervised point cloud completion framework, CSG-PCC, which leverages self-learned complete structures to guide the completion process. Our method achieves the extraction of complete structures and local details from incomplete point clouds through feature disentanglement and dual-Ievel contrastive learning. Experiments demonstrate that the point clouds completed by our method are structure-completed and detail-preservinn and exhibit state-of-the-ar performance of self-supervised methods on both synthetic and real-world datasets.
Acknowledgments and Disclosure of Funding
This work was supported by the National Natural Science Foundation China (Grant No. 62176064, NO. 62472097), and AI for Science Foundation of Fudan University (FudanX24A1028),
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A Technical Appendices and Supplementary Material
A.1 Implementation Details
We use the encoder from PCN 38 and employ an MLP with two hidden layer as our decoder. Similar to OptDE [[I], we take two separate disentanglers. each implemented MLPs with hidden layer, to extract shape and style features. For the loss function, we set the Acompletions Aclusters Aiastance to 1, 0.1, and 0.01, respectively. The FPCC is computed every 50 backpropagation steps with weight of 0.1. Like P2C [Z]. we divide the incomplete point cloud into 64 patches, each containing 32 points. 32 and the temperature scaling parameter t is set to 0.4. We train separate network for each class using the Adam W optimizer with starting learning rate of 10-3 and weight decay of 10-3 for 300 epochs. The experiments were conducted four NVIDIA
The num of shape prototypes |M is
GeForce RTX 3090 GPUs with 24GB memory each.
A.2 Qualitative comparisons on the PCN dataset
We also present qualitative comparisons in Figure 6 on the PCN dataset. When trained with less data and more incomplete inputs, our method outperform existing self-supervised approaches in both completion quality and input faithfulness, corroborating the robustness of our approach.
CSG-PCC (Ours)
Ground Truth
Input
PCN
CRA-PCN
ACL-SPC
P2C
Figure 6: Qualitative comparisons on the PCN dataset. The dashed line categorizes the methods based whether they utilize complete point clouds for supervision.
A.3 Visualization of prototypes.
concatenate the learned shape prototypes with zero vectors and pass them through the decoder to obtain the point clouds corresponding to the prototypes. By visualizing these point clouds, as shown in Figure we can observe that the learned shape prototypes are structurally complete.
A.4 Hyperparameter Selection.
We conduct empirical study to investigate the impact of hyperparameters.
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Chair
Plane
Figure Visualization of prototypes.
Table 5: The effect of number of shape prototypes. Results reported CD-&2 scaled by 10a.
Category
0.1
0.4
0.7
Cab
21.9
18.2
19.6
Cha
15.1
13.7
14.4
Lam
17.5
15.8
16.2
Sof
30.2
28.6
29.1
Avg
20.2
19.1
19.8
Impact of the number of shape prototypes. In partieular, examine the impact of the number of shape prototypes on the model's performance. The results show in Table 5 We observe that the optimal number of prototypes varies across different categories. However, we the prototype number to 32 for all categories for simplicity, yielding results superior to existing self-supervised methods.
Ground
Input
Result
Result
Result
Result 4
Result
Truth
Figure 8: Visualization of multimodal point cloud completion.
impact of the temperature parameter. Additionally, we investigate the impact of the temperature parameter / the prototype projection module on performance Table We test three different values of ( across four categories, observing that the network achieves optimal performance when / = 0.4. Higher values cause all negative samples to be treated uniformly with lacking discrimination, whereas lower values lead to insufficient reference similar complete structures.
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Table 6: The effect of temperature values. Results reported in CD-&2 & scaled by 10
Category
16
32
64
Cab
18.7
17.8
18.2
21.4
Cha
14.8
14.5
13.7
13.9
Lam
16.9
16.4
15.8
14.9
Sof
29.7
28.1
28.6
29.7
Avg
20.3
19.2
19.1
20.0
Table 7: Complexity and efficiency analysis in terms of the number of parameters (Params) and frames per second (fps) with the average Chamfer Distance CD-12 + on the 3D-EPN dataset as references.
Method
Params.
fpst
Avg.CD-RA
PCN
38
4. 4.1M
20.6
7.4
CRA-PCN 27
22.3M
14.7
3.7
Inv (3
40.1M
0.03
23.6
ACL-SPC 17
8.1M
17.4
31.6
P2C
23.9M
21.3
14.1
CSG-PCC(Ours)
27.1M
19.5
12.2
A.5
Complexity and Efficiency Analysis.
Additionally, we conduct complexity and efficiency analyses Table While our method entails moderate parameter increase (due to two disentanglers and prototype memory bank) and slight inference speed reduction compared to P2C, the resultant performance gains justify this trade-off.
A.6 Multimodal Point Cloud Completion.
Furthermore, due to the design of shape prototypes, concatenate the style feature with different shape prototypes to generate different completed point clouds, i.c., multimodal point cloud completion 333. The multimodal point cloud completion results shown in Figure R It can be observed that our method produces diverse yet plausible completion results for single incomplete input, which demonstrates potential of our methods for multimodal point cloud completion tasks.
22


Reviews:
[{"review_id": "ZU2tWGikhq", "reviewer": "Reviewer_KtxY", "summary": "The authors proposed a self-supervised method for Point Cloud Completion. This approach explicitly learns complete structures directly from incomplete point clouds, eliminating the need for complete training data. It employs cluster-level contrastive learning to extract complete-structure-guided shape features and instance-level learning to capture style details. This integrated design ensures generated point clouds achieve both shape completion and detail preservation. Extensive experiments on synthetic and real-world datasets demonstrate that the method significantly outperforms state-of-the-art self-supervised approaches.", "questions": "1.Figure 1 occupies disproportionate space for limited information. Clearly illustrating the distinctions between the proposed method and prior approaches in Figure 1 would enable readers to grasp this paper’s motivation more rapidly.\n2. Within which component of the overall framework (Figure 2) is the Feature Permutation Consistency Constraint incorporated? The authors state they \"randomly select two incomplete point clouds, Pp1 and Pp2\". Does this explicitly indicate that Pp1 and Pp2 may be selected from distinct categories? If sampled from different categories, would this introduce biases or affect experimental outcomes?\n3.What network architectures constitute the two disentanglers in the Feature Disentanglement Module? How can we interpret that one models shape information while the other models detail information?\n4.Which component in the Pipeline corresponds to the \"lgg\" section mentioned in line 173? It is recommended that the PPM module be presented in a separate, more detailed figure.\n5.In Section 3.3, labeling the approach employing CD loss as \"Instance-level Contrastive Learning\" may not be rigorously accurate. Adopting an alternative term would not undermine the novelty of this work.\n6. The abbreviated loss terms (Lcom, Lins) are used throughout the paper, while Figure 2 employs full terms. This inconsistency is inappropriate. Consistency should be maintained.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The method proposed in this paper is novel. It directly relies on prototype-based contrastive learning to infer the complete structure from incomplete point clouds within training data. This is an interesting and reasonable methodology. The paper demonstrates a well-organized structure and comprehensive experiments. However, certain sections lack clarity, such as the correspondence between modules, loss functions, and the figures, specifically concerning the \"Feature Disentanglement Module\" and the \"Lagg\" parts.", "was_revised": true, "final_justification": "Most of my concerns have been addressed, and I will maintain my positive rating.", "rebuttal": "Thank you very much for your positive feedback and valuable suggestions regarding our paper. We greatly appreciate your recognition of our work. Your comments are crucial in helping us improve the clarity and overall quality of our manuscript. We carefully address your suggestions and make the necessary revisions to enhance the paper. Below, we provide point-by-point responses to your comments.\n\n**Q1:** Figure 1 occupies disproportionate space for limited information. Clearly illustrating the distinctions between the proposed method and prior approaches in Figure 1 would enable readers to grasp this paper’s motivation more rapidly. \n\nThank you for your insightful suggestion regarding Figure 1. We plan to revise Figure 1 by spliting it two subfigures. The left subfigure  presents the pipeline of the prior methods, while the right one will expand the current figure to a self-supervised point cloud completion pipeline, which is guided by a **complete structure reconstruction module**.  This revised subfigures will use the **complete structure reconstruction module** to explicitly illustrate the distinctions between our method and prior approaches, thereby more effectively highlighting both the motivation and novelty of our approach. \n As the rebuttal process does not permit the submission of revised figures or PDFs, we are unable to provide the updated figure at this stage. However, we will ensure that the revised figure is included in the camera-ready version if accepted.\n\n**Q2:** Within which component of the overall framework (Figure 2) is the Feature Permutation Consistency Constraint incorporated? The authors state they \"randomly select two incomplete point clouds, Pp1 and Pp2\". Does this explicitly indicate that Pp1 and Pp2 may be selected from distinct categories? If sampled from different categories, would this introduce biases or affect experimental outcomes? \n\n**Regarding the FPCC integration:**\n\nThe Feature Permutation Consistency Constraint (FPCC) is designed to facilitate the disentanglement of shape and style features, i.e., giueding the training of shape and style disentanglers in Figure 2.  Specifically, as shown in Figure 3, $P_{recombined}$ is generated based on $P_p^{(1)}$ and $P_p^{(2)}$. It has the same shape as  $P_p^{(1)}$, so we use the shape disentangler in Figure 2 to extract shape features from both and ensure shape feature consistency by minimizing the distance of these two features (See the first term of Eq.(1) . Similarly, $P_{recombined}$ has the same style as  $P_p^{(2)}$, so we use the style disentangler in Figure 2 to extract style features from both and ensure style feature consistency by minimizing the distances of these two features in the second term of Eq.(1) . The combination of these yields the FPCC loss defined in Eq.(1). This loss is part of the overall loss function and is used for model training, as shown in Eq.(9). To clarify this in our framework, in the revised version of Figure 2, we will explicitly highlight the encoder and the disentanglers with a bounding box and use an arrow to indicate their connection to the FPCC loss. This visual enhancement would make the role and integration of FPCC within our overall pipeline more apparent. \n\n**Regarding the selection of $P_{p}^{\\(1\\)}$ and $P_{p}^{\\(2\\)}$:**\n\nSimilar to prior self-supervised approaches, our method trains a separate network for each category, for example, chair or plane. Thus, $P_{p}^{\\(1\\)}$ and $P_{p}^{\\(2\\)}$ are always randomly selected from different samples within the same category, rather than across categories. Moreover, combining samples from the same category is more reasonable, for instance, combining a plane and a chair would yield unnatural results. Consequently, there is no risk of introducing cross-category bias, and our experimental design ensures that the FPCC is applied within a consistent semantic context.\n\n**Q3:** What network architectures constitute the two disentanglers in the Feature Disentanglement Module? How can we interpret that one models shape information while the other models detail information?\n \n**Network Architectures of the Disentanglers:**\n\nBoth disentanglers are implemented as Multi-Layer Perceptrons (MLPs) with a single hidden layer, following the implementation in OptDE (as cited in [11] of our manuscript). \n\n**Interpretation of Shape and Detail (Style) Modeling:**\n\nIn our framework, “shape” refers to the global structure of a point cloud, which is captured by the skeleton points obtained through farthest point sampling. In contrast, “detail” (or “style”) refers to the local geometric features, represented by the K-nearest neighbor points around each skeleton point. Based on this setup, we generate $P_{recombined}$ from $P_p^{(1)}$ and $P_p^{(2)}$, as shown in Figure 3. As we mentioned in **Q2** , the $P_{recombined}$ has the same shape as $P_p^{(1)}$, so we use the shape disentangler to extract shape features from both and ensure shape feature consistency. Similarly,  $P_{recombined}$ has the same style as  $P_p^{(2)}$, so we use the style disentangler to extract style features from both and ensure style feature consistency. These two consistency constraint encourage each disentangler to focus on its designated attribute: the shape disentangler models the global structure, while the style disentangler models local details.\n\nTo further enhance clarity, we will update Figure 3 in our revised manuscript to explicitly include both disentanglers. Additionally, we will revise the caption of Figure 3 to more clearly articulate the purpose of FPCC, emphasizing its role in enforcing effective feature disentanglement.\n\n**Q4:** Which component in the Pipeline corresponds to the \"lagg\" section mentioned in line 173? It is recommended that the PPM module be presented in a separate, more detailed figure. \n\nIn our current implementation, we have combined the losses $ \\cal L_{agg}$ and $ \\cal L_{info}$ into a unified loss term $ \\cal L_{cluster}$, as shown in Eq.(6), which is explicitly depicted in the Pipeline (Figure 2). As a result, the $ \\cal L_{agg}$ is not independently illustrated, which may have affected the clarity of our presentation. We fully agree with your suggestion to present the PPM module in a separate, dedicated figure. In the revised manuscript,  we will simplify the component of the PPM in Figure 2 and introduce a new, standalone figure for the PPM, similar to Figure 3. In this new figure, we will detail the computation pipelines for both loss terms ($ \\cal L_{agg}$ and $ \\cal L_{info}$), thereby providing a clearer and more comprehensive illustration of the PPM’s structure and its associated losses.\n\n**Q5:** In Section 3.3, labeling the approach employing CD loss as \"Instance-level Contrastive Learning\" may not be rigorously accurate. Adopting an alternative term would not undermine the novelty of this work. \n\nThank you for your valuable feedback and for recognizing the novelty of our approach. Our initial use of the term **“Instance-level Contrastive Learning”** was motivated by the design of the $\\cal L_{instance}$ : this loss computes the distance between two completion results generated from different partial observations of the same instance. By minimizing this discrepancy, we encourage the network to treat these two outputs as positive pairs, promoting consistency and the preservation of instance-specific details. \n\nNevertheless, we agree that the use of the term “contrastive learning” may not be rigorously precise in this context, as our approach does not involve negative pairs or the standard contrastive learning framework. To improve clarity without affecting the novelty of our work, we  would  adopt the alternative term such as **“instance-consistency loss”** .\n\n**Q6:** The abbreviated loss terms (Lcom, Lins) are used throughout the paper, while Figure 2 employs full terms. This inconsistency is inappropriate. Consistency should be maintained.\n\nThanks. We will revise the manuscript so that the loss terms are presented consistently throughout. Specifically, we will use $\\cal L_{cluster}$ and $\\cal L_{instance}$ both in the main text and in all figures."}, {"review_id": "NZmVCSiJTp", "reviewer": "Reviewer_WfDZ", "summary": "This paper presents a joint training framework for object pose optimizaton and object reconstruction. The authors argue that leveraging 3D shapes as a \"free lunch\" can mutually benefit both branches, but the contribution brought by such design is questionable.", "questions": "see weakness", "limitations": "see weakness", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "1. Limited novelty in methodology. The core design of this paper lies in two modules, Reconstructed Shape Supervision (RSS) and Predicted Shape Guided Sampling (PSGS). \n\nRSS, as claimed in this paper, presents a better integration compared to SUP-NeRF[Guo et al., ECCV 2024] by putting pose estimation module into an interative loop. Such improvements are largely implementation details. SUP-NeRF has already integrated shape featured into pose training through shortcut operations. \n\nFor PSGS, the piror shape comes from a poes estimation branch. This shape is not detailed, or even category-level detailed. It is only a low-res point-cloud directly fed into NeRF optimization pipeline, while no new optimization strategy is provided. Such design is not different from alleviating a canonical shape prior. The benefit brought by joint training is not clear beyond providing more trainable parameters.\n\n\n2. Insufficient Theoretical Insight on \"Free Lunch\": The paper heavily leans on empirical demonstrations without offering any meaningful theoretical analysis or deeper justification of why the proposed modules should perform better. Especially, the \"free lunch\" claim is not properly explained or extentively tested to make a rigorous point. \n\n3. Insufficient Experiments: The generalization capability is only demonstrated on KITTI, much concern is raised here regarding how effective this method could be when facing more difficult situations in driving. It is also mainly evaluated against AutoRF [Müller et al., CVPR 2022] and SUP-NeRF. It is recommended to see more comparisons with BootInv[Pavllo et al., CVPR 2023], which treats the canonical shape as an anchor, then jointly optimizes pose and deformation. And also NeuroCS[Min et al., CVPR 2023], which introduces category-level shape priors via a learned canonical coordinate system, far more structured compared to FreeShape.", "was_revised": true, "final_justification": "I sincerely apologize for this oversight. As I have to review multiple papers, I may have pasted the wrong review comments, and only realized this upon reading the rebuttal. Since the rebuttal period has ended, and in the interest of fairness, I have decided not to submit any further review comments and choose my score as boardline accpt-the average score of other reviewers-to make the further discussion be fair.\n\nPlease the ACs refer to the suggestions of the other reviewers when making your decision. Thank you again, and I apologize for any inconvenience caused.", "rebuttal": "We regret to inform you that **the review comments we have received are not related to our submission**. \nThe current comments pertain to a paper on object pose optimization and object reconstruction, discussing modules such as Reconstructed Shape Supervision (RSS), Predicted Shape Guided Sampling (PSGS), and the “free lunch” claim. However, our submission focuses on self-supervised point cloud completion, and none of these modules or terms are present in our work.\n\nWe have attempted to seek assistance from the AC during the rebuttal period to obtain the correct set of review comments. Unfortunately, by the end of the first phase of rebuttal, we have not received the appropriate comments.  **As a result, we are currently unable to respond meaningfully to the provided comments.** We hope you can understand the difficult situation we are in. Our lack of response to the review comments is not intentional, but rather due to the fact that the comments do not pertain to our submission."}, {"review_id": "P5h29UPFm8", "reviewer": "Reviewer_kAqv", "summary": "This paper addresses the self-supervised point cloud completion task. The authors identified the absence of complete structure difficulty in the point cloud data during training. They proposed a method to train the model with self learned complete structure produced from the novel self-supervised complete structure reconstruction module. Additionally, a contrastive learning approach at both the cluster and instance-level to extract shape features guided by the complete structure and to capture style features, respectively. The experiment shows the effectiveness of the proposed method.", "questions": "1. How was dual-level contrastive learning was illustrated in Figure 2\n\n2. In the visual results, for example the lamp (row 2) of Figure 4, the result shows some outlier points. What could be the reason? Is this common in the results? Does this imply some design faults of this framework?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. previous self-supervised completion methods mainly employs patch-based methods to construct pseudo complete structure supervisions, while this paper proposed to create such complete structure supervision by aggregating point clouds in clusters created from contrastive learning, which is interesting and promising.\n\n2. The design of the complete structure reconstruction is interesting, and the formulation of point cloud features as shape and style is reasonable.\n\n3. The Feature Permutation Consistency Constraint (FPCC) is also interesting.\n\n4. The performance and visual results look good. It shows more predicted point clouds in missing regions compared to previous methods.\n\nWeaknesses:\n\n1. An illustrative figure should be self-contained. However, it is difficult to clearly understand the FPCC if only read Figure 3. The authors may need to enhance the caption and content of this figure.\n\n2.  The last paragraph of the introduction section is too long. The authors may separate the \"We conducted experiments ...\" as another paragraph.; The limitation section is important, should be part of the main paper.\n\n3. typos: Figure 2, there is an additional space between Similarity and Calculation; and missing space in \"b)Prototype\"; Table 1 caption,  3d-EPN should be 3D-EPN", "was_revised": true, "final_justification": "My final justification is maintaining my positive rating.", "rebuttal": "We are honored that our work has received your recognition. Your insightful comments and constructive suggestions are extremely valuable for further enhancing our manuscript. In the following, we carefully address the weaknesses and questions you pointed out, and describe the corresponding revisions we will make.\n\n**Weakness 1:** An illustrative figure should be self-contained. However, it is difficult to clearly understand the FPCC if only read Figure 3. The authors may need to enhance the caption and content of this figure.\n\nThank you for your constructive suggestion. In our framework, “shape” refers to the global structure of a point cloud, which is captured by the skeleton points obtained through farthest point sampling. In contrast, “detail” (or “style”) refers to the local geometric features, represented by the K-nearest neighbor points around each skeleton point. Based on this setup, we generate $P_{recombined}$ from $P_p^{(1)}$ and $P_p^{(2)}$, as shown in Figure 3. The $P_{recombined}$ has the same shape as $P_p^{(1)}$, so we use the shape disentangler in Figure 2 to extract shape features from both and ensure shape feature consistency in Eq.(1). Similarly, the $P_{recombined}$ has the same style as  $P_p^{(2)}$, so we use the style disentangler to extract style features from both and ensure style feature consistency. The combination of these two consistencies yields the FPCC loss in Eq.(1). This loss is part of the overall loss function and is used for model training, as shown in Eq.(9)\n\nTo be self-contained, we will revise Figure 3 by explicitly illustrating the feature extraction process of the disentanglers, making the flow of information clearer. Additionally, we will enhance the caption to clearly state that the primary purpose of the FPCC is to guide feature disentanglement. Furthermore, in Figure 2, we will highlight the encoder and disentanglers using bounding boxes and visually indicate their relationship to the FPCC, thereby clarifying their connections within the feature disentanglement module. Through these improvements, we aim to make Figures 2 and 3 both more self-contained and mutually complementary, thereby enhancing the overall coherence and clarity of the manuscript.\n\n**Weakness 2:** The last paragraph of the introduction section is too long. The authors may separate the \"We conducted experiments ...\" as another paragraph.; The limitation section is important, should be part of the main paper.\n\nWe will start a new paragraph from “We conducted experiments …” in the introduction section to improve readability. In addition, we will move the Limitation section to Section 5 and the Conclusion to Section 6, as you suggested. \n\n**Weakness 3:** typos: Figure 2, there is an additional space between Similarity and Calculation; and missing space in \"b)Prototype\"; Table 1 caption, 3d-EPN should be 3D-EPN\n\nWe will thoroughly proofread the manuscript and correct all such issues. We appreciate your attention to details, which helps to improve the quality of our paper.\n\n**Q 1:** How was dual-level contrastive learning was illustrated in Figure 2\nThank you for your question regarding the illustration of dual-level contrastive learning in Figure 2. The dual-level contrastive learning in our method refers to the instance-level and cluster-level contrastive strategies separately :\n1. **Instance-level contrastive learning** is reflected in Figure 2 by the two parallel branches, where each branch processes an incomplete point cloud derived from the same original sample. The completion results of these two branches are expected to be consistent; therefore, we treat the outputs from the same input as positive pairs for contrastive learning, ultimately formulated as $\\cal L_{instance}$ in Figure 2, whose detailed formulation is given in Eq.(7). This is visually indicated by the “Pull Close” arrow in Figure 2.\n2. **Cluster-level contrastive learning**  is illustrated in Figure 2 by PPM. Here, the structure of the point cloud is mapped into the Prototype Memory Bank. We use contrastive learning to pull the shape features closer to their corresponding prototype, while pushing them away from other prototypes, thus capturing the complete structures at the cluster level through $\\cal L_{cluster}$ in Eq.(6). This is visually indicated by the “Pull Close” and “Pull Away” arrow in Figure 2.\n\n**Q 2:** In the visual results, for example the lamp (row 2) of Figure 4, the result shows some outlier points. What could be the reason? Is this common in the results? Does this imply some design faults of this framework?\n\nThank you for your insightful question. We have carefully analyzed the causes and have updated our explanation as follows:\nThe occurrence of outlier points in the lamp category primarily arises from the intrinsic properties of this category. Compared to categories such as chairs, airplanes, or tables, lamps 1) have significantly fewer samples; 2) and much higher intra-class variation (e.g., table lamps, chandeliers, pendant lamps); 3) lamps often exhibit complex structures, low symmetry;4) in many cases, the centroid of the sample is not located within the main body of the object. These factors make the completion task for lamps particularly challenging and can lead to the appearance of outlier points in the results.\n\nThe occurrence of such outlier points is not unique to our method but is also observed in previous approaches (as can be seen in Figure 4). This suggests that it is not a design fault of our framework, but rather a reflection of the challenging nature of this category and the limited data available. As discussed in the limitations section, our method, like other self-supervised point cloud completion techniques, is sensitive to the quality and diversity of the training data. Nevertheless, our prototype memory mechanism helps to cluster complete structural features, thereby reducing the negative impact of sub-category variations within the lamp class. As demonstrated in Figure 4, our approach substantially reduces the number and severity of outlier points compared to previous methods.\n\nWe appreciate the reviewer’s suggestion, and we will further discuss the limitations and potential future directions for addressing outlier points in the lamp category in the revised manuscript if accepted."}, {"review_id": "KG74JRDdxY", "reviewer": "Reviewer_CKyN", "summary": "In this work, the authors propose a method to train a point cloud completion network without GT completed point clouds for supervision. During training, the input point cloud is firstly repeatedly corrupted into two homologous but different point clouds processed with identical completion network, whose completed results would be used to calculated instance-level contrastive loss.\nIn the completion network, each corrupted input point cloud is disentangled into two branches: the shape branch and style branch. The shape branch extracts shape features from the FPS sampled points, and completed them by \"clustering\" the 3D partial points in the training set with a memory bank, where the style branch help preserves details from existing partial geometry.", "questions": "Please check the weaknesses section, thanks.", "limitations": "Yes, in the appendix.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The proposed method does not rely on complete 3D ground truth for training. Instead, it leverages diverse observations aggregated from the training set, reducing the need for extensive labeled data.\n\n2. The method demonstrates superior performance on both synthetic datasets (3D-EPN, PCN) and real-world data (ScanNet), highlighting its effectiveness and generalizability.\n\nWeaknesses:\n\n1. The core of the method relies on shape clustering across the training set to populate a memory bank with shared features. However, this process is heavily dependent on the diversity and scale of the dataset. For example, if only rectangular tables are present during training, the model may struggle to complete round tables or generalize to different object categories like chairs.\n\n2. The motivation for incorporating instance-level contrastive learning remains unclear and would benefit from a more thorough explanation. Clarifying its role and contribution to performance would strengthen the paper.\n\n3. Except the mentioned methods in the main paper, there are some other papers working on zero-shot point cloud completion based on 2D pre-trained diffusion priors, which also do not require any training on completed data, such as [1, 2]. Some comparisons or discussions would be essential.\n\n[1] Point cloud completion with pretrained text-to-image diffusion models\n\n[2] ComPC: Completing a 3D Point Cloud with 2D Diffusion Priors\n\n4. The paper could be further strengthened by including more comparisons on datasets that lack ground truth, such as KITTI. This would better demonstrate the practical utility of the method in real-world scenarios.\n\n5. The manuscript would benefit from additional proofreading and editing for clarity and accuracy. For instance:\n\n(1) Line 176 refers to \"RCD in Eq. 3,\" but it appears to be standard Chamfer Distance instead.\n\n(2) The term \"UCD\" is used without definition—please clarify if this refers to Uniform Chamfer Distance.\n\n(3) In the caption of Figure 5, \"scanNet\" is incorrectly written and should be corrected.", "was_revised": true, "final_justification": "Thank you to the authors for their response. After reading the rebuttal, I remain generally positive about the work. I would also strongly encourage the authors to clearly address the mentioned concerns in the revised version to further strengthen the paper. Thank you.", "rebuttal": "We greatly appreciate your positive feedback and thoughtful suggestions regarding our paper. In particular, the paper you recommended provides a feasible direction for the future improvement of our method. Furthermore, your comments on both the content and the experiments have helped us further enhance the clarity and persuasiveness of our paper. Next, we will respond to your suggestions point by point.\n\n**Weakness 1:** The core of the method relies on shape clustering across the training set to populate a memory bank with shared features. However, this process is heavily dependent on the diversity and scale of the dataset. For example, if only rectangular tables are present during training, the model may struggle to complete round tables or generalize to different object categories like chairs. \n\nYes, we have acknowledged this limitation in our paper in Appendix.7. Our method is indeed constrained by the diversity and scale of the training data. However, this is a common challenge faced by current self-supervised point cloud completion methods. Nevertheless, the significance of self-supervised approaches lies in the fact that we can collect incomplete point clouds as training data and then perform completion, without the need for complete point clouds as supervision. Besides, in such scenarios (e.g., when the training data lacks diversity in specific shapes or categories), our method can mitigate the risk of being misled by clustered prototypes by simply skipping the PPM. Meanwhile, the instance-level contrastive learning component remains effective, ensuring reasonable completion performance.\n\nConsidering the methods you recommended (based on 2D pre-trained diffusion priors), we agree that leveraging powerful 2D priors or cross-modal pre-training strategies could be a promising way to alleviate the reliance on extensive 3D training data. In response to your suggestion, we will add a discussion in the appendix (see **Weakness 3** ) to highlight this potential improvement, and outline how future research could benefit from such approaches. \n\n**Weakness 2:** The motivation for incorporating instance-level contrastive learning remains unclear and would benefit from a more thorough explanation. Clarifying its role and contribution to performance would strengthen the paper.\n\nThank you for highlighting the need for a clearer motivation regarding the incorporation of instance-level contrastive learning. We address your concerns as follows:\n\n* **Clarification of Role:** In our pipeline, i.e., Figure 2, the two incomplete point clouds in the dual-branch network are generated from a single input instance. Consequently, the completed results from both branches should ideally be consistent. By encouraging the network to minimize the discrepancy between these two outputs, i.e., $\\cal L_{instance}$ shown in Eq.(7). We treat them as positive pairs in a contrastive sense, thereby promoting consistency and preservation of instance-specific details.\n* **Contribution to Performance:** The effect of the proposed instance-level contrastive loss ($ \\cal L_{instance}$ ) is demonstrated in our ablation study (Section 4.4). As shown in Table 4, adding $\\cal L_{instance}$ leads to more significant improvements in categories with richer details, such as lamp and chair, compared to cabinet and sofa. This supports our claim that instance-level contrastive learning is particularly beneficial for retaining local details in the completion results. \n\n**Weakness 3:** Except the mentioned methods in the main paper, there are some other papers working on zero-shot point cloud completion based on 2D pre-trained diffusion priors, which also do not require any training on completed data, such as [1, 2]. Some comparisons or discussions would be essential.\n\n[1] Point cloud completion with pretrained text-to-image diffusion models\n\n[2] ComPC: Completing a 3D Point Cloud with 2D Diffusion Prior\n\nThank you for pointing out these two outstanding works, which are indeed highly valuable references. Their utilization of 2D pre-trained diffusion priors effectively reduces the reliance on large-scale 3D training datasets. However, it is important to note that these two methods belong to a different branch of point cloud completion from our method together with the baselines in this paper.  So a direct comparison with our method may not be entirely fair, particularly in terms of efficiency. Both [1] and [2] employ optimization-based algorithms, which perform extensive optimization during the completion process for a given point cloud. In contrast, our method together with the baselines learn a completion network during training and only perform a single forward pass in completing each point cloud. As reported in [2], completing a single sample requires 15 minutes on an A6000 GPU, while [1] requires up to 1950 minutes per sample. In contrast, our method completes 19.5 samples per second on an RTX 3090 GPU. Even without considering hardware differences, this means our method is orders of magnitude faster than [2]. Such discrepancy in efficiency between [1-2] and our method implies that they would be used in different applications with differenet requirements on latency. \n \nFurthermore, we believe that methods [1–2] can be combined with our method to improve efficiency, where our method can provide a coarse completion result as input to optimization-based methods such as [2], which can then finetune the final results.\n\nWe will incorporate the above discussion into the revised version of our paper to provide a more comprehensive and balanced perspective.\n\n**Weakness 4:** The paper could be further strengthened by including more comparisons on datasets that lack ground truth, such as KITTI. This would better demonstrate the practical utility of the method in real-world scenarios.\nThank you for your constructive suggestion.  As recommended, we have conducted experiments on the car category of the KITTI dataset processed by the pcl2pcl [5]. The dataset contains 7,569 samples, with an average of 527 points per incomplete point cloud. The results are presented in the table below:\n| Method | Supervision | UCD  | UHD  | RCD  |\n|------|-----|------|------|------|\n| PCN[38]        | Supervised | 37.3 | 14.0 | 6.57 |\n| Inv [39]       |  Unpaired  | 3.16 |   7.11  |  1.34 |\n| ACL-SPC [17]   | Self-supervised | 9.75 |  6.89  |  3.95 |\n| P2C [7]        | Self-supervised |3.61 | 6.98 | 1.22 |\n| CSG-PCC(Ours)  |     Self-supervised    |3.38 | 6.17 | 1.21 |\n\nAs shown, our method outperforms existing self-supervised point cloud completion methods on the KITTI dataset. The superior performance of Inv[39] on the UCD metric compared to our method stems from its optimization-based nature, as it explicitly employs the UCD loss during inference, as mentioned in the main text on lines 239–240. Regarding supervised methods, our approach achieves a notably greater improvement on KITTI compared to our experiments on the ScanNet dataset as shown in Table 3. This is because, for ScanNet, we used only 550 chairs and 552 tables for training and evaluation, while for KITTI, we utilized 7,569 cars. As the amount of training data increases, the domain gap becomes more pronounced for supervised methods. That is, as we discussed in line 31 to 32 in the main text, they tend to suffer from limited generalization ability for they are trained on virtual data and tested on real-world data. In contrast, our method can be directly trained on real incomplete point clouds, thereby mitigating the impact of domain differences. This further demonstrates the practical utility and robustness of our approach in real-world settings.\n\nWe will include these experimental results and analyses in the revised version of the paper.\n\n**Weakness 5:**  The manuscript would benefit from additional proofreading and editing for clarity and accuracy. For instance:\n(1) Line 176 refers to \"RCD in Eq. 3,\" but it appears to be standard Chamfer Distance instead.\n(2) The term \"UCD\" is used without definition—please clarify if this refers to Uniform Chamfer Distance.\n(3) In the caption of Figure 5, \"scanNet\" is incorrectly written and should be corrected.\n\nWe will thoroughly proofread the manuscript and correct all typos. We appreciate your attention to details, which helps to improve the quality of our paper."}]

Author rebuttal:

Meta-review: This paper presents a self-supervised framework for point cloud completion that explicitly reconstructs complete structures from incomplete data by employing dual-level contrastive learning to disentangle shape and style features. Reviewers find the approach novel and empirically strong across synthetic and real-world benchmarks, showing clear improvements over prior self-supervised methods. Main concerns of this work lie in presentation clarity, missing ablations and details, and limited discussion of related zero-shot or diffusion-based methods. Since these issues are well addressed and clarified during rebuttal, the contribution of this work is considered meaningful and the paper is clearly above the acceptance threshold.
Decision: Accept (spotlight)