深度情報を用いた物体のマルチクラス検出とセグメンテーション
Multi-Class Detection and Segmentation of Objects in Depth
RGB-D画像を用いて複数クラスの物体を検出・セグメンテーションし、3D位置を推定する手法を提案した論文。深度情報の活用により検出のロバスト性と3D位置精度が向上することを示した。
著者: Cheng Zhang, Hedvig Kjellstrom
分類: cs.CV, cs.RO
原文アブストラクト
The quality of life of many people could be improved by autonomous humanoid robots in the home. To function in the human world, a humanoid household robot must be able to locate itself and perceive the environment like a human; scene perception, object detection and segmentation, and object spatial localization in 3D are fundamental capabilities for such humanoid robots. This paper presents a 3D multi-class object detection and segmentation method. The contributions are twofold. Firstly, we present a multi-class detection method, where a minimal joint codebook is learned in a principled manner. Secondly, we incorporate depth information using RGB-D imagery, which increases the robustness of the method and gives the 3D location of objects -- necessary since the robot reasons in 3D space. Experiments show that the multi-class extension improves the detection efficiency with respect to the number of classes and the depth extension improves the detection robustness and give sufficient natural 3D location of the objects.
