Digging into Intrinsic Contextual Information for High-fidelity 3D Point Cloud Completion

TL;DR

This paper introduces a high-fidelity 3D point cloud completion method that leverages intrinsic short and long-range contextual information from partial point clouds to improve detail and accuracy, outperforming existing methods.

Contribution

The paper proposes a novel PCC approach that integrates short and long-range contextual information using mixed sampling, surface freezing, and similarity modeling modules for enhanced detail recovery.

Findings

Outperforms state-of-the-art methods in high-fidelity PCC tasks.

Effectively utilizes intrinsic contextual information for detailed shape reconstruction.

Demonstrates robustness and superior quality in extensive experiments.

Abstract

The common occurrence of occlusion-induced incompleteness in point clouds has made point cloud completion (PCC) a highly-concerned task in the field of geometric processing. Existing PCC methods typically produce complete point clouds from partial point clouds in a coarse-to-fine paradigm, with the coarse stage generating entire shapes and the fine stage improving texture details. Though diffusion models have demonstrated effectiveness in the coarse stage, the fine stage still faces challenges in producing high-fidelity results due to the ill-posed nature of PCC. The intrinsic contextual information for texture details in partial point clouds is the key to solving the challenge. In this paper, we propose a high-fidelity PCC method that digs into both short and long-range contextual information from the partial point cloud in the fine stage. Specifically, after generating the coarse point cloud via a diffusion-based coarse generator, a mixed sampling module introduces short-range contextual information from partial point clouds into the fine stage. A surface freezing modules safeguards points from noise-free partial point clouds against disruption. As for the long-range contextual information, we design a similarity modeling module to derive similarity with rigid transformation invariance between points, conducting effective matching of geometric manifold features globally. In this way, the high-quality components present in the partial point cloud serve as valuable references for refining the coarse point cloud with high fidelity. Extensive experiments have demonstrated the superiority of the proposed method over SOTA competitors. Our code is available at https://github.com/JS-CHU/ContextualCompletion.