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A Direct Semi-Exhaustive Search Method for Robust, Partial-to-Full Point Cloud Registration

arXiv 2502.00115 ↗First posted 31 Jan 2025cs.RO cs.CVcited by 0

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Abstract (arXiv)

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).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 31 Jan 2025.

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References · 22

  1. 2019Deep Closest Point: Learning Representations for Point Cloud Registrationcited by 318
  2. 2020PREDATOR: Registration of 3D Point Clouds with Low Overlapcited by 287
  3. 2020TEASER: Fast and Certifiable Point Cloud Registrationcited by 274
  4. 2019PointNetLK: Robust & Efficient Point Cloud Registration using PointNetcited by 231
  5. 2020Deep Global Registrationcited by 228
  6. 2022Geometric Transformer for Fast and Robust Point Cloud Registrationcited by 200
  7. 2020RPM-Net: Robust Point Matching using Learned Featurescited by 181
  8. 2018PPFNet: Global Context Aware Local Features for Robust 3D Point Matchingcited by 180
  9. 2020D3Feat: Joint Learning of Dense Detection and Description of 3D Local Featurescited by 175
  10. 2016Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registrationcited by 159
  11. 2021CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registrationcited by 151
  12. 2019PRNet: Self-Supervised Learning for Partial-to-Partial Registrationcited by 146
10 more
  1. 2020SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registrationcited by 141
  2. 2018PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptorscited by 140
  3. 2020Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondencescited by 108
  4. 2021Robust Point Cloud Registration Framework Based on Deep Graph Matchingcited by 96
  5. 2020DeepGMR: Learning Latent Gaussian Mixture Models for Registrationcited by 91
  6. 2020Fast and Robust Iterative Closest Pointcited by 67
  7. 2021Deep Hough Voting for Robust Global Registrationcited by 53
  8. 2021OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registrationcited by 50
  9. 2021A Learned Stereo Depth System for Robotic Manipulation in Homescited by 14
  10. 2020Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registrationcited by 12

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