CorsNet: 3D Point Cloud Registration by Deep Neural Network

Akiyoshi Kurobe, Yusuke Sekikawa, Kohta Ishikawa, Hideo Saito

研究成果: Article査読

16 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号8978671
ページ(範囲)3960-3966
ページ数7
ジャーナルIEEE Robotics and Automation Letters
5
3
DOI
出版ステータスPublished - 2020 7

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 生体医工学
  • 人間とコンピュータの相互作用
  • 機械工学
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用
  • 制御と最適化
  • 人工知能

フィンガープリント

「CorsNet: 3D Point Cloud Registration by Deep Neural Network」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル