SGOR: Outlier Removal by Leveraging Semantic and Geometric Information for Robust Point Cloud Registration

TL;DR

This paper presents SGOR, a novel outlier removal method that leverages semantic and geometric information for robust point cloud registration, significantly improving accuracy and robustness in challenging scenarios.

Contribution

The paper introduces a new outlier removal approach that combines semantic-geometric consistency and regional voting to enhance robustness and accuracy in point cloud registration.

Findings

Achieved 22.5 percentage points improvement in registration recall.

Demonstrated superior robustness in outdoor datasets.

Improved correspondence quality through semantic-geometric consistency.

Abstract

In this paper, we introduce a new outlier removal method that fully leverages geometric and semantic information, to achieve robust registration. Current semantic-based registration methods only use semantics for point-to-point or instance semantic correspondence generation, which has two problems. First, these methods are highly dependent on the correctness of semantics. They perform poorly in scenarios with incorrect semantics and sparse semantics. Second, the use of semantics is limited only to the correspondence generation, resulting in bad performance in the weak geometry scene. To solve these problems, on the one hand, we propose secondary ground segmentation and loose semantic consistency based on regional voting. It improves the robustness to semantic correctness by reducing the dependence on single-point semantics. On the other hand, we propose semantic-geometric consistency for outlier removal, which makes full use of semantic information and significantly improves the quality of correspondences. In addition, a two-stage hypothesis verification is proposed, which solves the problem of incorrect transformation selection in the weak geometry scene. In the outdoor dataset, our method demonstrates superior performance, boosting a 22.5 percentage points improvement in registration recall and achieving better robustness under various conditions. Our code is available.