LiDAR2Map: オンラインカメラ蒸留によるLiDARベース意味地図構築の強化
LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation
LiDARのBEV特徴を多スケール化するデコーダと、カメラからLiDARへオンライン蒸留する手法を組み合わせ、LiDARベースの意味地図構築精度を大幅に向上させた研究。
著者: Song Wang, Wentong Li, Wenyu Liu, Xiaolu Liu, Jianke Zhu
分類: cs.CV, cs.RO
原文アブストラクト
Semantic map construction under bird's-eye view (BEV) plays an essential role in autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D observations to project the captured 3D features onto BEV space inherently. However, the vanilla LiDAR-based BEV feature often contains many indefinite noises, where the spatial features have little texture and semantic cues. In this paper, we propose an effective LiDAR-based method to build semantic map. Specifically, we introduce a BEV feature pyramid decoder that learns the robust multi-scale BEV features for semantic map construction, which greatly boosts the accuracy of the LiDAR-based method. To mitigate the defects caused by lacking semantic cues in LiDAR data, we present an online Camera-to-LiDAR distillation scheme to facilitate the semantic learning from image to point cloud. Our distillation scheme consists of feature-level and logit-level distillation to absorb the semantic information from camera in BEV. The experimental results on challenging nuScenes dataset demonstrate the efficacy of our proposed LiDAR2Map on semantic map construction, which significantly outperforms the previous LiDAR-based methods over 27.9% mIoU and even performs better than the state-of-the-art camera-based approaches. Source code is available at: https://github.com/songw-zju/LiDAR2Map.
