A Direct Semi-Exhaustive Search Method for Robust, Partial-to-Full Point Cloud Registration
This addresses the challenge of robust, partial-to-full point cloud registration for applications in robotics and computer vision, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of point cloud registration without correspondences by proposing a direct semi-exhaustive search method that leverages GPU parallelism, achieving state-of-the-art performance on the ModelNet40 benchmark and demonstrating robustness in a real-world robotics application.
Point cloud registration refers to the problem of finding the rigid transformation that aligns two given point clouds, and is crucial for many applications in robotics and computer vision. The main insight of this paper is that we can directly optimize the point cloud registration problem without correspondences by utilizing an algorithmically simple, yet computationally complex, semi-exhaustive search approach that is very well-suited for parallelization on modern GPUs. Our proposed algorithm, Direct Semi-Exhaustive Search (DSES), iterates over potential rotation matrices and efficiently computes the inlier-maximizing translation associated with each rotation. It then computes the optimal rigid transformation based on any desired distance metric by directly computing the error associated with each transformation candidate $\{R, t\}$. By leveraging the parallelism of modern GPUs, DSES outperforms state-of-the-art methods for partial-to-full point cloud registration on the simulated ModelNet40 benchmark and demonstrates high performance and robustness for pose estimation on a real-world robotics problem (https://youtu.be/q0q2-s2KSuA).