LCF3D: 自動運転における3D物体検出のための堅牢でリアルタイムなレイトカスケード融合フレームワーク
LCF3D: A Robust and Real-Time Late-Cascade Fusion Framework for 3D Object Detection in Autonomous Driving
RGB画像の2D検出とLiDAR点群の3D検出をレイト融合とカスケード融合で組み合わせ、LiDARの誤検出を除去しつつ見逃しを回復する3D物体検出フレームワークを提案。
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著者: Carlo Sgaravatti, Riccardo Pieroni, Matteo Corno, Sergio M. Savaresi, Luca Magri, Giacomo Boracchi
分類: cs.CV, cs.RO
原文アブストラクト
Accurately localizing 3D objects like pedestrians, cyclists, and other vehicles is essential in Autonomous Driving. To ensure high detection performance, Autonomous Vehicles complement RGB cameras with LiDAR sensors, but effectively combining these data sources for 3D object detection remains challenging. We propose LCF3D, a novel sensor fusion framework that combines a 2D object detector on RGB images with a 3D object detector on LiDAR point clouds. By leveraging multimodal fusion principles, we compensate for inaccuracies in the LiDAR object detection network. Our solution combines two key principles: (i) late fusion, to reduce LiDAR False Positives by matching LiDAR 3D detections with RGB 2D detections and filtering out unmatched LiDAR detections; and (ii) cascade fusion, to recover missed objects from LiDAR by generating new 3D frustum proposals corresponding to unmatched RGB detections. Experiments show that LCF3D is beneficial for domain generalization, as it turns out to be successful in handling different sensor configurations between training and testing domains. LCF3D achieves significant improvements over LiDAR-based methods, particularly for challenging categories like pedestrians and cyclists in the KITTI dataset, as well as motorcycles and bicycles in nuScenes. Code can be downloaded from: https://github.com/CarloSgaravatti/LCF3D.
関連論文
- LiRaFusion: 3D物体検出のための深層適応型LiDAR-レーダー融合3D物体検出/センサーフュージョン
- GOOD: LiDARとカメラの物体候補を統合する汎用最適化ベースの3D物体検出フレームワーク3D物体検出/センサーフュージョン
