T-CorresNet: Template Guided 3D Point Cloud Completion with Correspondence Pooling Query Generation Strategy

TL;DR

T-CorresNet introduces a novel template-guided approach with correspondence pooling for 3D point cloud completion, effectively handling incompleteness by generating dynamic queries and refining the complete shape.

Contribution

The paper presents a new method combining spherical templates and correspondence pooling to improve point cloud completion accuracy.

Findings

Outperforms state-of-the-art methods on multiple benchmarks.

Effectively handles incomplete point clouds with limited perspectives.

Uses a coarse-to-fine completion paradigm with a FoldingNet decoder.

Abstract

Point clouds are commonly used in various practical applications such as autonomous driving and the manufacturing industry. However, these point clouds often suffer from incompleteness due to limited perspectives, scanner resolution and occlusion. Therefore the prediction of missing parts performs a crucial task. In this paper, we propose a novel method for point cloud completion. We utilize a spherical template to guide the generation of the coarse complete template and generate the dynamic query tokens through a correspondence pooling (Corres-Pooling) query generator. Specifically, we first generate the coarse complete template by embedding a Gaussian spherical template into the partial input and transforming the template to best match the input. Then we use the Corres-Pooling query generator to refine the coarse template and generate dynamic query tokens which could be used to predict the complete point proxies. Finally, we generate the complete point cloud with a FoldingNet following the coarse-to-fine paradigm, according to the fine template and the predicted point proxies. Experimental results demonstrate that our T-CorresNet outperforms the state-of-the-art methods on several benchmarks. Our Codes are available at https://github.com/df-boy/T-CorresNet.