Unsupervised Point Cloud Completion through Unbalanced Optimal Transport
Point Cloud Completion;Unpaired Point Cloud Completion;Unbalanced optimal transport;Optimal transport
Yes
No ethics review needed.
1.	Compared with the diffusion based mehtods, what are the advantages of the proposed mehtod?
2.	Compared with USSPA, the proposed method has comparable (lower) performance in some categories. What is the reason?
3.	Is it possiable to complete unseen categories?
4.	Do the authors think the CD or F_score are the best metrics for evaluating 3D shape completion?
1.	I like the 3D shape completion topic, and the pipeline is carefully designed.
2.	The proposed method is evaluated on the dataset proposed in USSPA and performs better than SOTAs, although some important SOTAs are missing.
3.	The paper is clear and easy to follow.
This paper an unpaired point cloud completion approach based on the unbalanced optimal transport map. The key idea is to formulate the unpaired point cloud completion task as the optimal transport problem and investigate the optimal cost function for this task, and introduce an unbalanced optimal transport framework for addressing the class imbalance problem. Experimental results show the proposed method achieves state-of-the-art performance in unpaired point cloud completion.
1.	The authors should test more categories to explore the effects of class imbalance.
2.	The authors should conduct more datasets for demonstrating the effectiveness of the proposed mehtods, such as PCN datasets.
3.	Some important SOTA methods for 3D shape completion are missing. The authors should compare and discuss them with the proposed method.
[1] ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion
[2] 3D Shape Generation and Completion Through Point-Voxel Diffusion
4. The computational cost should be analyzed and compared with the other methods.
5. The authors are encouraged to provide code for reimplementation.
Yes
No ethics review needed.
According to the weakness part, please clarify the choice of cost function and report more results on other datasets. Besides, the source mixture trick seems interesting but not well-explained. Can you give more explanation and/or more insights about this trick?
1. Novelty: UOT-UPC is the first model to apply the unbalanced optimal transport map to unpaired point cloud completion.

2. Robustness to Class Imbalance: The UOT framework allows UOT-UPC to effectively handle class imbalance. Experiments demonstrated that UOT-UPC maintained consistent performance across various class imbalance ratios, better than other models like USSPA and OT-UPC.

3. Better Performance: UOT-UPC achieves better performance on both single-category and multi-category settings on the dataset proposed by Ma et al. 2023.
This paper introduces UOT-UPC, an unpaired point cloud completion model based on the unbalanced optimal transport (UOT) map. The key idea is to train a model that learns the UOT map between the distribution of incomplete point clouds and the distribution of complete point clouds. This approach leverages the UOT framework's ability to address the class imbalance problem commonly found in unpaired point cloud completion datasets. The paper also identifies the InfoCD cost function as particularly well-suited for unpaired point cloud completion tasks. Experiments show that using InfoCD leads to better performance compared to other cost functions like l2, L2-Chamfer distance, and one-directional L2-Chamfer distance.
1. Limited Cost Function Exploration: The paper's claim of identifying the "optimal" cost function can be challenged. While the authors compare four different cost functions (l2, L2-Chamfer distance (cdl2), one-directional L2-Chamfer distance (cdl2fwd), and InfoCD), this is a relatively small selection of potential options. Other cost functions might exist that could yield even better performance. The study primarily relies on the ShapeNet dataset for evaluating these cost functions. 

2. Dataset Dependence: The experiments primarily focus on a single dataset proposed in Ma et al. 2023, despite the availability of other datasets in the field. This raises concerns about the generalizability of UOT-UPC's performance to other datasets. Evaluating the model on a wider variety of datasets would strengthen the paper's conclusions and provide a more comprehensive understanding of UOT-UPC's capabilities and limitations.
Yes
No ethics review needed.
Whether the assumption in optimal transport map is satisfied by the given data should be discussed.
1. They propose to deal with the unbalance problem in point cloud completion.
2. They present better results than the competitors.
This paper propose an unbalanced optimal transport for unpaired point cloud completion. They treat the completion problem as mapping incomplete set to complete set and utilize InfoCD for cost function. They show results for single-category and multiple-category unpaired point cloud completion and conducted many ablation study.
1. Optimal Transport Map is utilized to map the incomplete set to complete set. However, this assume the set of incomplete point cloud and the set of complete set should be complete set. For instance, this work utilized the complete point cloud from ShapeNet and partial point cloud from ScanNet. The original dataset only align the ShapeNet model with the real scans, however, the assumption in Optimal Transport is hard to be satisfied.
2. Experiments are only conducted in data from Scan2CAD. More experiments should be conducted on MatterPort3D or KITTI like "ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion" and ModelNet or 3D-FUTURE like "CloudMix: Dual Mixup Consistency for Unpaired Point Cloud Completion".
3. More related works like "ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion" and "CloudMix: Dual Mixup Consistency for Unpaired Point Cloud Completion" should be compared.
We propose an unpaired point cloud completion model based on the unbalanced optimal transport map.
@inproceedings{
anonymous2024unsupervised,
title={Unsupervised Point Cloud Completion through Unbalanced Optimal Transport},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Nh1w3ZnDaH},
note={under review}
}
Unpaired point cloud completion explores methods for learning a completion map from unpaired incomplete and complete point cloud data. In this paper, we propose a novel approach for unpaired point cloud completion using the unbalanced optimal transport map, called Unbalanced Optimal Transport Map for Unpaired Point Cloud Completion (UOT-UPC). We demonstrate that the unpaired point cloud completion can be naturally interpreted as the Optimal Transport (OT) problem and introduce the Unbalanced Optimal Transport (UOT) approach to address the class imbalance problem, which is prevalent in unpaired point cloud completion datasets. 
Moreover, we analyze the appropriate cost function for unpaired completion tasks. This analysis shows that the InfoCD cost function is particularly well-suited for this task.
Our model is the first attempt to leverage UOT for unpaired point cloud completion, achieving competitive or superior results on both single-category and multi-category datasets. In particular, our model is especially effective in scenarios with class imbalance, where the proportions of categories are different between the incomplete and complete point cloud datasets.
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Point Cloud Completion Unpaired Point Cloud Completion Unbalanced optimal transport Optimal transport
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Unsupervised Point Cloud Completion through Unbalanced Optimal Transport
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