CorsNet: 3D Point Cloud Registration by Deep Neural Network

Akiyoshi Kurobe, Yusuke Sekikawa, Kohta Ishikawa, Hideo Saito

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)


Point cloud registration is a key problem for robotics and computer vision communities. This represents estimating a rigid transform which aligns one point cloud to another. Iterative closest point (ICP) is a well-known classical method for this problem, yet it generally achieves high alignment only when the source and template point cloud are mostly pre-aligned. If each point cloud is far away or contains a repeating structure, the registration often fails because of being fallen into a local minimum. Recently, inspired by PointNet, several deep learning-based methods have been developed. PointNetLK is a representative approach, which directly optimizes the distance of aggregated features using gradient method by Jacobian. In this paper, we propose a point cloud registration system based on deep learning: CorsNet. Since CorsNet concatenates the local features with the global features and regresses correspondences between point clouds, not directly pose or aggregated features, more useful information is integrated than the conventional approaches. For comparison, we also developed a novel deep learning approach (DirectNet) that directly regresses the pose between point clouds. Through our experiments, we show that CorsNet achieves higher accuracy than not only the classic ICP method, but also the recently proposed learning-based proposal PointNetLK and DirectNet, including on seen and unseen categories.

Original languageEnglish
Article number8978671
Pages (from-to)3960-3966
Number of pages7
JournalIEEE Robotics and Automation Letters
Issue number3
Publication statusPublished - 2020 Jul


  • Computer vision for other robotic applications
  • deep learning in robotics and automation
  • perception for grasping and manipulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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