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

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.

• The paper introduces a two-stage consensus filtering method that uses one-, two-, and three-point RANSAC to quickly eliminate outliers and compute accurate poses.
• It leverages length and angle consistency constraints along with an iterative reweighted least squares step to refine correspondence selection and pose estimation.
• Experiments on KITTI and ETH datasets show up to three orders of magnitude speedup, underscoring its potential for real-time 3D registration in robotics and autonomous navigation.

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

The paper "RANSAC Back to SOTA: A Two-stage Consensus Filtering for Real-time 3D Registration" advances point cloud registration (PCR), specifically focusing on the challenges posed by outliers in correspondence-based methods. The authors present a novel two-stage consensus filtering (TCF) approach to enhance the performance of RANSAC, traditionally recognized for its inability to efficiently handle high outlier ratios.

Key Contributions

The primary innovation lies in a hierarchical application of RANSAC, strategically divided into three distinct phases:

1. One-point RANSAC: This initial step leverages length consistency to identify a consensus set through length constraints, aiming to eliminate substantial outliers swiftly.
2. Two-point RANSAC: Angle consistency is introduced in this phase to refine the consensus set further. This stage addresses any residual outliers left by the initial filtering by ensuring geometric congruency via angular constraints.
3. Three-point RANSAC: Once an accurate consensus set is established, a three-point RANSAC computes a coarse pose based on the distances of transformed correspondences. This step integrates the refined correspondences into more accurate pose estimation, minimizing the necessity for numerous iterations.

The incorporation of an iterative reweighted least squares (IRLS) step further refines pose estimation, effectively reducing errors introduced by any remaining outliers.

Numerical Validation

The proposed methodology underwent rigorous evaluation on the large-scale KITTI and ETH datasets. The experiments demonstrated substantial improvements, showcasing speedups up to three orders of magnitude over previous methods like MAC, while maintaining comparable accuracy and recall rates. Specifically, on challenging datasets like KITTI, where the inlier ratios are notably low, the proposed method was able to boost the recall significantly, emphasizing its robustness and efficiency.

Implications and Future Directions

The practical implications of this research are substantial, particularly in applications demanding real-time processing, such as robotic navigation and autonomous vehicles. By reducing computational demands and enhancing efficiency without sacrificing accuracy, this method could redefine real-time PCR applications.

Theoretically, the study reinforces the importance of structured filtering in RANSAC methodologies, emphasizing how an additional abstraction layer can mitigate the negative impacts of high-dimensional outlier spaces. This could inspire future research to explore similar stratagems across other domains requiring robust outlier removal mechanisms.

Looking forward, potential developments could focus on integrating machine learning approaches to dynamically adjust the parameters governing each stage of the consensus filtering process, thereby enhancing adaptability to diverse datasets and environmental conditions. Additionally, extending this method to multi-modal sensor data could further broaden its applicability across various fields in robotics and computer vision.

In conclusion, this paper contributes effectively to the discourse on robust 3D registration, reinforcing RANSAC's relevance in contemporary computational methodologies while pushing the boundaries of speed and efficiency.