Imagine with the Teacher: Complete Shape in a Multi-View Distillation Way (2501.19270v1)

Abstract: Point cloud completion aims to recover the completed 3D shape of an object from its partial observation caused by occlusion, sensor's limitation, noise, etc. When some key semantic information is lost in the incomplete point cloud, the neural network needs to infer the missing part based on the input information. Intuitively we would apply an autoencoder architecture to solve this kind of problem, which take the incomplete point cloud as input and is supervised by the ground truth. This process that develops model's imagination from incomplete shape to complete shape is done automatically in the latent space. But the knowledge for mapping from incomplete to complete still remains dark and could be further explored. Motivated by the knowledge distillation's teacher-student learning strategy, we design a knowledge transfer way for completing 3d shape. In this work, we propose a novel View Distillation Point Completion Network (VD-PCN), which solve the completion problem by a multi-view distillation way. The design methodology fully leverages the orderliness of 2d pixels, flexibleness of 2d processing and powerfulness of 2d network. Extensive evaluations on PCN, ShapeNet55/34, and MVP datasets confirm the effectiveness of our design and knowledge transfer strategy, both quantitatively and qualitatively. Committed to facilitate ongoing research, we will make our code publicly available.

The paper introduces a novel approach to 3D point cloud completion titled "Imagine with the Teacher: Complete Shape in a Multi-View Distillation Way," which centers on the development of a View Distillation Point Completion Network (VD-PCN). The methodology proposed involves leveraging the teacher-student paradigm inherent to knowledge distillation to enhance the reconstruction of incomplete 3D shapes using multi-view representations.

Key Contributions

1. Multi-View Distillation Framework:

• VD-PCN Design: The network is designed to utilize multi-view CNNs, where depths maps of the partial input point cloud are processed. This structure draws inspiration from 2D image processing efficiency, capitalizing on the orderliness and transferability from mature 2D networks to 3D vision tasks.
• Multi-View Encoder: Incorporates both intra-view fusion and inter-view enhancement layers to aggregate information across different perspectives, developing a comprehensive global understanding of the object's structure.

2. Knowledge Transfer Strategy:

• Teacher-Student Model: The paper implements a knowledge distillation model where a pre-trained teacher (trained on complete depth maps and partial point clouds) guides the student model. The teacher's well-established mapping from partial to complete shapes enables the student to emulate its performance.
• Feature Alignment: Knowledge is distilled at both feature and point cloud levels with carefully designed loss functions, allowing the student model to effectively infer and fill in the missing parts of the point cloud.

3. Dual-Modality Decoder:

• This component integrates both 2D and 3D features for point cloud reconstruction. The approach compensates for information loss occurring during projection and pooling processes by reintroducing 3D point cloud data, thereby enhancing detail recovery and mitigating over-smoothing.

Experimental Validation

The paper reports that VD-PCN achieves state-of-the-art performance across several benchmarks, including PCN, ShapeNet55, and MVP datasets, substantiating its efficacy both qualitatively and quantitatively:

• On the PCN dataset, VD-PCN surpasses existing methods such as SVDFormer, achieving an L1 Chamfer distance of 6.32, underscoring the benefit of its unique transfer learning approach.
• Performance evaluation on ShapeNet-55 reveals consistently superior results across difficulty levels, with VD-PCN achieving a notable L2 Chamfer distance reduction to 0.70.
• Tests on the MVP dataset further demonstrate VD-PCN's capacity to manage high-resolution point clouds effectively, enhancing shape completion fidelity compared to other leading architectures like FBNet.

Additional Insights

The proposed method successfully leverages well-established 2D techniques in the field of 3D point cloud processing. The paper highlights VD-PCN's capacity to outperform complex point-based architectures while maintaining competitive computational efficiency. However, the authors acknowledge limitations related to the synthetic nature of the current dataset, suggesting a need for future real-world applicability studies.

Conclusion

This paper suggests that VD-PCN, by combining multi-view projection techniques with knowledge distillation, provides a robust framework for advancing point cloud completion tasks. By incorporating comprehensive experimental evaluations, the authors demonstrate significant improvements in both qualitative and quantitative outcomes over existing approaches, suggesting a promising direction for further advancements in the field. The planned public release of the code will facilitate additional exploration and extend the applicability of their methodology.