P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds (2307.14726v1)

Abstract: Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of the same object for learning. In contrast to previous approaches, we present Partial2Complete (P2C), the first self-supervised framework that completes point cloud objects using training samples consisting of only a single incomplete point cloud per object. Specifically, our framework groups incomplete point clouds into local patches as input and predicts masked patches by learning prior information from different partial objects. We also propose Region-Aware Chamfer Distance to regularize shape mismatch without limiting completion capability, and devise the Normal Consistency Constraint to incorporate a local planarity assumption, encouraging the recovered shape surface to be continuous and complete. In this way, P2C no longer needs multiple observations or complete point clouds as ground truth. Instead, structural cues are learned from a category-specific dataset to complete partial point clouds of objects. We demonstrate the effectiveness of our approach on both synthetic ShapeNet data and real-world ScanNet data, showing that P2C produces comparable results to methods trained with complete shapes, and outperforms methods learned with multiple partial observations. Code is available at https://github.com/CuiRuikai/Partial2Complete.

Self-Supervised Point Cloud Completion: Advancements and Implications

The paper "P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds" introduces a novel approach to point cloud completion leveraging self-supervision, a stark deviation from traditional methods that require complete point clouds or multiple observations of the same object. The approach, termed Partial2Complete (P2C), aims to reconstruct a complete 3D shape using only a single partial point cloud per object during training, which holds significant implications for the fields of computer vision and 3D data processing, especially in scenarios where obtaining complete datasets is arduous or costly.

Technical Contribution and Methodology

The P2C framework represents a pioneering effort in self-supervised learning for point cloud completion. By employing a method of grouping incomplete point clouds into local patches and predicting masked regions, the framework is able to infer a structural prior from an array of different partial objects within the same category. This is pivotal because it circumvents the necessity of not only complete ground truth data but also mitigates the challenges associated with acquiring multiple viewpoints of the same object.

Central to the P2C's methodological advancements are two key components: the Region-Aware Chamfer Distance (RCD) and the Normal Consistency Constraint (NCC). RCD is introduced to tackle the limitations of traditional distance measures, which either impose constraints on completion capacity or lead to mismatched predictions. By dynamically generating regions centered on skeleton points, RCD allows the model to align more naturally with the underlying complete shape of an object, thus facilitating better completion performance without requiring explicit supervision. The NCC, on the other hand, incorporates a local planarity assumption which enhances the fidelity of surface continuity and completeness in the reconstructed shapes. These innovations highlight the paper's contribution to refining the efficacy of shape completion using limited and partial information.

Evaluation and Results

The evaluation of P2C spans both synthetic data (3D-EPN and PCN datasets) and real-world data (ScanNet dataset), showcasing the model's versatility across diverse settings. The quantitative results are notable, with P2C demonstrating comparable performance to fully supervised models when evaluated against synthetic datasets and outperforming existing unpaired and weakly-supervised methods. Specifically, the method achieves significant improvements in Chamfer Distance scores across categories in the 3D-EPN dataset and performs competitively on the PCN dataset despite the absence of paired training data traditionally used in supervised setups.

In real-world scenarios, P2C maintains robustness, surpassing models trained on complete examples in terms of reconstruction fidelity. This underscores the adaptability of P2C to real-world point cloud data, which often presents with noise and incomplete information—challenges that supervised methods, generally reliant upon synthetic or complete data, may struggle to overcome.

Implications and Future Directions

The introduction of P2C opens several avenues for future research and application. The capacity to perform effective point cloud completion with minimal observational input heralds a new efficient paradigm for 3D reconstruction tasks in domains such as autonomous driving, robotics, and AR/VR, where real-time, adaptive solutions are paramount. Moreover, the methodological framework presented is likely to stimulate further investigation into self-supervised learning techniques for other forms of 3D data augmentation and manipulation tasks.

However, the impact of this research extends beyond mere point cloud completion. The underpinning principles of the work, especially the novel approaches to regularizing completion tasks without explicit supervision, can be extrapolated to other domains within machine learning and computer vision where data completeness and quality are persistent challenges.

In summation, the P2C framework provides a significant stride forward for self-supervised methodologies in point cloud processing. Its robust performance and innovative approaches to dealing with partial data sets not only elevate the state-of-the-art in point cloud completion but also pave a promising new direction for future explorations in the field of machine learning.