3D LiDAR点群からの全畳み込みネットワークによる車両検出
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
Velodyne 64E LiDARの距離データを2Dポイントマップに変換し、単一の2D全畳み込みネットワークで物体信頼度と3Dバウンディングボックスを同時に予測する車両検出手法を提案した。KITTIデータセットで最先端性能を達成。
著者: Bo Li, Tianlei Zhang, Tian Xia
分類: cs.CV, cs.RO
原文アブストラクト
Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method.
