PCR-99: A Practical Method for Point Cloud Registration with 99 Percent Outliers (2402.16598v6)

Abstract: We propose a robust method for point cloud registration that can handle both unknown scales and extreme outlier ratios. Our method, dubbed PCR-99, uses a deterministic 3-point sampling approach with two novel mechanisms that significantly boost the speed: (1) an improved ordering of the samples based on pairwise scale consistency, prioritizing the point correspondences that are more likely to be inliers, and (2) an efficient outlier rejection scheme based on triplet scale consistency, prescreening bad samples and reducing the number of hypotheses to be tested. Our evaluation shows that, up to 98% outlier ratio, the proposed method achieves comparable performance to the state of the art. At 99% outlier ratio, however, it outperforms the state of the art for both known-scale and unknown-scale problems. Especially for the latter, we observe a clear superiority in terms of robustness and speed.

• The paper introduces a deterministic 3-point sampling strategy that prioritizes inlier correspondences using pairwise scale consistency.
• It implements an efficient outlier rejection mechanism based on triplet scale consistency to reduce computational overhead.
• PCR-99 demonstrates robust performance on point clouds with up to 99% outliers, setting a new benchmark for challenging 3D registration tasks.

PCR-99: A Deterministic and Efficient Approach for Point Cloud Registration in Extreme Outlier Conditions

Introduction to PCR-99

Point Cloud Registration (PCR) is pivotal in various 3D computer vision applications ranging from autonomous navigation to augmented reality. The PCR-99 algorithm introduces a novel approach aimed at handling extreme outlier conditions with remarkable efficiency. It deploys a deterministic 3-point sampling strategy enhanced by two innovative mechanisms: an improved ordering of samples leveraging pairwise scale consistency and an efficient outlier rejection scheme utilizing triplet scale consistency.

Novel Contributions

PCR-99 distinguishes itself through two primary contributions:

• Deterministic Sampling with Prioritization: Utilizes a scoring system based on pairwise scale consistency to rank point correspondences. This system inherently prioritizes samples with a higher likelihood of being inliers, thereby enhancing the efficiency of the sampling process.
• Efficient Outlier Rejection Scheme: Before computing hypotheses based on sampled points, PCR-99 conducts a prescreening based on triplet scale consistency. This mechanism effectively discards unsuitable samples early in the process, yielding a significant reduction in computational overhead.

Theoretical and Practical Implications

PCR-99 extensively evaluates point cloud datasets, demonstrating robust performance up to an outlier ratio of 99% and showcasing superior efficiency and robustness over existing state-of-the-art methods, especially in unknown-scale problems. This algorithm sets a new benchmark for outlier tolerance in point cloud registration tasks, offering substantial theoretical insights into deterministic sampling methods and outlier rejection mechanisms in correspondence-based registration processes. Its practical implications extend to improving the reliability and speed of 3D mapping, object recognition, and localization in environments heavily contaminated with outliers.

Algorithm Evaluation and Results

PCR-99's evaluation reveals it consistently outperforms current methods in terms of robustness and speed, particularly at the extreme outlier ratio of 99%. Its advantage becomes more pronounced in unknown-scale registration problems, highlighting its potential as a go-to solution for challenging PCR tasks. Notably, the algorithm showcases minimal performance degradation even as the outlier ratio approaches 99%, underscoring its exceptional outlier handling capability.

Future Developments in AI and Point Cloud Processing

The introduction of PCR-99 opens multiple avenues for future research and development in generative AI and point cloud processing:

• Exploration of Hybrid Models: Combining deterministic sample prioritization with advanced machine learning models could further enhance outlier detection and inlier prioritization efficiency.
• Real-time Processing: Optimizing PCR-99 for real-time applications in robotics and augmented reality could revolutionize how machines interact with their environments.
• Scalability: Investigating the scalability of PCR-99's methodologies to handle increasingly large datasets is crucial for its adoption in large-scale 3D reconstruction projects.

Conclusion

PCR-99 represents a significant step forward in addressing the challenge of point cloud registration amidst extreme outlier conditions. Its novel deterministic sampling strategy and efficient outlier rejection mechanism pave the way for more reliable and swifter point cloud registration processes. As we continue to push the boundaries of what is possible in computer vision and 3D scene analysis, methodologies like PCR-99 will undoubtedly play a crucial role in shaping the future of technology and its applications.