ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion (2303.01979v3)

Abstract: Point cloud completion addresses filling in the missing parts of a partial point cloud obtained from depth sensors and generating a complete point cloud. Although there has been steep progress in the supervised methods on the synthetic point cloud completion task, it is hardly applicable in real-world scenarios due to the domain gap between the synthetic and real-world datasets or the requirement of prior information. To overcome these limitations, we propose a novel self-supervised framework ACL-SPC for point cloud completion to train and test on the same data. ACL-SPC takes a single partial input and attempts to output the complete point cloud using an adaptive closed-loop (ACL) system that enforces the output same for the variation of an input. We evaluate our proposed ACL-SPC on various datasets to prove that it can successfully learn to complete a partial point cloud as the first self-supervised scheme. Results show that our method is comparable with unsupervised methods and achieves superior performance on the real-world dataset compared to the supervised methods trained on the synthetic dataset. Extensive experiments justify the necessity of self-supervised learning and the effectiveness of our proposed method for the real-world point cloud completion task. The code is publicly available from https://github.com/Sangminhong/ACL-SPC_PyTorch

• The paper introduces an adaptive closed-loop mechanism that reliably reconstructs complete point clouds from partial inputs.
• The method employs self-supervised learning with a novel loss function combining weighted Chamfer distance and consistency loss.
• Experimental results on synthetic and real-world datasets demonstrate improved accuracy and generalization in 3D scene interpretation.

ACL-SPC: A Self-Supervised Approach for Robust Point Cloud Completion

The paper "ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion" presents a novel self-supervised framework designed to address the challenges in point cloud completion, specifically focusing on real-world applicability without relying on synthetic datasets or multi-view information. The proposed method, ACL-SPC, introduces an adaptive closed-loop system that robustly predicts complete point clouds from partial inputs, which are typically incomplete due to sensor limitations such as occlusions and angle restrictions.

Technical Contributions

1. Adaptive Closed-Loop (ACL) Mechanism: The core of the proposed methodology is the ACL system that ensures consistency in the output despite variations in input configurations. By applying this model, each partial input leads to the generation of a comparable complete point cloud, even when derived from different perspectives.
2. Self-Supervised Learning: Unlike traditional supervised or weakly-supervised methods that often rely on paired datasets, ACL-SPC operates on a self-supervised basis. It uniquely adapts by generating synthetic partial views from the initial complete reconstruction and optimizing the network with a consistency loss between these synthetic partial views and the original completion.
3. Loss Function Design: The framework employs a novel loss function strategy that combines a weighted Chamfer distance with a consistency loss. The Chamfer distance plays a dual role, acting as both a regularizer and a guide for optimizing point distribution in areas unexposed in the input, thereby enhancing the system's overall accuracy in completion tasks.

Evaluation and Experimental Results

The validity of the ACL-SPC is demonstrated across several datasets, both synthetic and real-world:

• On the ShapeNet synthetic dataset, ACL-SPC yields results competitive with state-of-the-art methods, achieving better coverage metrics, which are critical for evaluating the completion of missing areas in point clouds.
• In real-world scenarios using datasets such as SemanticKITTI, ScanNet, and Matterport3D, the model shows a marked improvement over existing unsupervised and even supervised methods, highlighting its capability to generalize well beyond its training domain.

Implications and Future Directions

The development of ACL-SPC has significant implications for applications reliant on 3D geometry data, such as autonomous vehicles and robotics, where real-time and accurate scene interpretation is crucial. The ability to perform point cloud completion without dependency on labeled datasets or multiple views is particularly advantageous, making this approach adaptable in diverse operational environments.

Looking forward, further advancements could be made by incorporating mechanisms to enhance precision and distribution uniformity of output point clouds, addressing the current limitation of noise and redundancy. Additionally, extending this framework to other forms of point cloud restoration, like denoising and upsampling, might offer broad applications in 3D data processing and analysis.

This paper marks a significant step towards versatile and efficient point cloud processing, emphasizing adaptability and practicality in deployment, characteristics quintessential for modern computer vision systems operating in dynamic, real-world settings.