RangeDet：距離画像ビューで挑むLiDAR 3D物体検出
RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection
LiDARの距離画像表現のみを用いたアンカーフリー単段階3D物体検出器を提案し、スケール変動と座標系の不整合を解消する3つの工夫で従来の距離画像手法を大幅に上回った。
著者: Lue Fan, Xuan Xiong, Feng Wang, Naiyan Wang, Zhaoxiang Zhang
分類: cs.CV, cs.RO
原文アブストラクト
In this paper, we propose an anchor-free single-stage LiDAR-based 3D object detector -- RangeDet. The most notable difference with previous works is that our method is purely based on the range view representation. Compared with the commonly used voxelized or Bird's Eye View (BEV) representations, the range view representation is more compact and without quantization error. Although there are works adopting it for semantic segmentation, its performance in object detection is largely behind voxelized or BEV counterparts. We first analyze the existing range-view-based methods and find two issues overlooked by previous works: 1) the scale variation between nearby and far away objects; 2) the inconsistency between the 2D range image coordinates used in feature extraction and the 3D Cartesian coordinates used in output. Then we deliberately design three components to address these issues in our RangeDet. We test our RangeDet in the large-scale Waymo Open Dataset (WOD). Our best model achieves 72.9/75.9/65.8 3D AP on vehicle/pedestrian/cyclist. These results outperform other range-view-based methods by a large margin (~20 3D AP in vehicle detection), and are overall comparable with the state-of-the-art multi-view-based methods. Codes will be public.
