Point Cloud Completion by Skip-attention Network with Hierarchical Folding (2005.03871v2)

Abstract: Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, the global representation often suffers from the information loss of structure details on local regions of incomplete point cloud. To address this problem, we propose Skip-Attention Network (SA-Net) for 3D point cloud completion. Our main contributions lie in the following two-folds. First, we propose a skip-attention mechanism to effectively exploit the local structure details of incomplete point clouds during the inference of missing parts. The skip-attention mechanism selectively conveys geometric information from the local regions of incomplete point clouds for the generation of complete ones at different resolutions, where the skip-attention reveals the completion process in an interpretable way. Second, in order to fully utilize the selected geometric information encoded by skip-attention mechanism at different resolutions, we propose a novel structure-preserving decoder with hierarchical folding for complete shape generation. The hierarchical folding preserves the structure of complete point cloud generated in upper layer by progressively detailing the local regions, using the skip-attentioned geometry at the same resolution. We conduct comprehensive experiments on ShapeNet and KITTI datasets, which demonstrate that the proposed SA-Net outperforms the state-of-the-art point cloud completion methods.

Skip-Attention Network for Point Cloud Completion

This paper addresses a fundamental problem in 3D computer vision known as point cloud completion. Specifically, it targets completing the missing regions of 3D shapes represented by point clouds, a common issue due to limitations of scanning devices that often yield sparse and incomplete data. The authors propose a novel solution through the Skip-Attention Network (SA-Net), which significantly enhances the ability to generate complete 3D models with more detailed local structures.

Methodological Innovations

The primary contribution of this work is twofold:

1. Skip-Attention Mechanism: Unlike prior approaches that rely heavily on a global shape representation, the skip-attention mechanism selectively focuses on local region details of the point clouds. This mechanism transmits key geometric information from the local regions of incomplete point clouds across different resolutions. This selective attention helps in revealing the underlying processing of completion in an interpretable manner.
2. Structure-Preserving Decoder with Hierarchical Folding: To utilize thoroughly the information encoded by the skip-attention mechanism, the authors introduce a novel decoder design. This hierarchical folding decoder preserves the structure of a completed point cloud as generated from upper layers and progressively refines local regions using the skip-attentioned geometry. This approach overcomes the pitfalls of earlier methods, which either fail to preserve local detail or introduce redundancy through generalized approaches like U-Net skip connections.

Experimental Results

The efficacy of SA-Net is demonstrated through extensive experiments on the ShapeNet and KITTI datasets. The paper reports that SA-Net outperforms existing state-of-the-art methods such as TopNet, PCN, and FoldingNet in terms of Chamfer Distance, a widely recognized metric in point cloud completion tasks. The improvements are attributed to the network's ability to maintain global shape consistency while capturing intricate local details. Furthermore, SA-Net established superior qualitative results on the KITTI dataset, augmenting its applicability to real-world data.

Implications and Future Directions

Practically, the proposed method enhances the processing of raw 3D data directly obtained from scanning technologies, which is pivotal in applications spanning robotics, virtual reality, and augmented reality. Theoretically, this paper advances the understanding of how localized attention mechanisms can be efficiently harnessed within an encoding-decoding framework to boost 3D shape inference capabilities.

A potential future direction would involve further refining the network's learning ability to generalize efficiently across a broader range of 3D shapes and occlusions. Moreover, exploring the combination of skip-attention with other advanced neural architectures could yield even more powerful models capable of addressing increasingly complex 3D reconstruction challenges.

Conclusion

In conclusion, this work articulately presents a novel skip-attention network that demonstrates significant improvements in point cloud completion tasks. By focusing on local detail preservation and reducing information redundancy, SA-Net represents a meaningful advancement in neural network architectures for 3D data processing, with promising implications for further research and practical applications in AI.