PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers (2108.08839v1)

Abstract: Point clouds captured in real-world applications are often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many practical applications. In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion. By representing the point cloud as a set of unordered groups of points with position embeddings, we convert the point cloud to a sequence of point proxies and employ the transformers for point cloud generation. To facilitate transformers to better leverage the inductive bias about 3D geometric structures of point clouds, we further devise a geometry-aware block that models the local geometric relationships explicitly. The migration of transformers enables our model to better learn structural knowledge and preserve detailed information for point cloud completion. Furthermore, we propose two more challenging benchmarks with more diverse incomplete point clouds that can better reflect the real-world scenarios to promote future research. Experimental results show that our method outperforms state-of-the-art methods by a large margin on both the new benchmarks and the existing ones. Code is available at https://github.com/yuxumin/PoinTr

Overview of "PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers"

"PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers" introduces a novel approach to point cloud completion by leveraging transformer architecture. Point cloud data, often incomplete due to sensor limitations and occlusions, requires efficient completion techniques to reconstruct detailed 3D structures. This paper presents a method that conceptualizes point cloud completion as a set-to-set translation problem, utilizing a transformer encoder-decoder model to generate complete point clouds from partial inputs.

Transformer-Based Architecture

The proposed model, PoinTr, capitalizes on the transformer architecture, renowned for its effectiveness in sequence processing tasks. By translating point clouds into sequences of point proxies through position embeddings, the model employs transformers to exploit structural dependencies and spatial relationships. The encoder-decoder framework facilitates learning of both global and local geometric relationships essential for accurate point cloud completion.

Geometry-Aware Enhancements

A key innovation of the paper is the introduction of a geometry-aware transformer block. This block augments the standard transformer with inductive biases about 3D geometric structures, explicitly encoding local geometric relations among point cloud subsets. By integrating kNN models to capture these interactions, the geometry-aware block enhances the transformer's ability to preserve intricate spatial details.

Benchmarks and Evaluation

To assess the model's performance, two challenging benchmarks—ShapeNet-55 and ShapeNet-34—were developed, featuring a diverse range of object categories and varying levels of incompleteness. These benchmarks reflect real-world conditions more accurately, encompassing diverse viewpoints and object types. PoinTr demonstrated superior performance against state-of-the-art methods, achieving significant improvements in Chamfer Distance metrics across these benchmarks.

Practical Implications and Future Directions

The potential applications of PoinTr span various domains, including autonomous vehicles, robotics, and virtual reality, where accurate 3D reconstructions from incomplete data are critical. The transformer architecture's ability to generalize across categories also hints at promising applications in unseen environments, as evidenced by its competitive performance on novel category tasks in ShapeNet-34.

Speculative Outlook

The integration of geometric awareness into transformer models might usher in advancements in other 3D vision tasks, such as object recognition and scene understanding. As transformer models continue to evolve, incorporating domain-specific knowledge like geometric relationships could enhance their applicability across diverse problem spaces.

This work sets a foundation for exploring transformers in 3D data processing, offering a robust framework for researchers aiming to extend transformer utility in computer vision and beyond. Future research could explore further optimizations in computational cost and investigate the model's adaptability to real-time applications.