RANSAC Back to SOTA: A Two-stage Consensus Filtering for Real-time 3D Registration

TL;DR

This paper introduces a two-stage consensus filtering method that significantly improves the speed and accuracy of point cloud registration using RANSAC, making it suitable for real-time applications in robotics and computer vision.

Contribution

The proposed two-stage consensus filtering enhances RANSAC by reducing iterations and improving accuracy, achieving state-of-the-art speed and robustness in 3D registration.

Findings

Up to three orders of magnitude speedup over existing methods

Maintains high registration accuracy and recall

Effective on large-scale datasets like KITTI and ETH

Abstract

Correspondence-based point cloud registration (PCR) plays a key role in robotics and computer vision. However, challenges like sensor noises, object occlusions, and descriptor limitations inevitably result in numerous outliers. RANSAC family is the most popular outlier removal solution. However, the requisite iterations escalate exponentially with the outlier ratio, rendering it far inferior to existing methods (SC2PCR [1], MAC [2], etc.) in terms of accuracy or speed. Thus, we propose a two-stage consensus filtering (TCF) that elevates RANSAC to state-of-the-art (SOTA) speed and accuracy. Firstly, one-point RANSAC obtains a consensus set based on length consistency. Subsequently, two-point RANSAC refines the set via angle consistency. Then, three-point RANSAC computes a coarse pose and removes outliers based on transformed correspondence's distances. Drawing on optimizations from one-point and two-point RANSAC, three-point RANSAC requires only a few iterations. Eventually, an iterative reweighted least squares (IRLS) is applied to yield the optimal pose. Experiments on the large-scale KITTI and ETH datasets demonstrate our method achieves up to three-orders-of-magnitude speedup compared to MAC while maintaining registration accuracy and recall. Our code is available at https://github.com/ShiPC-AI/TCF.