MonoPair: 隣接ペアの空間関係を利用した単眼3D物体検出
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships
隣接する物体ペアの位置と3D距離を不確実性付きで予測し、非線形最小二乗で統合最適化することで、遮蔽された物体の単眼3D検出精度を向上させた研究。
著者: Yongjian Chen, Lei Tai, Kai Sun, Mingyang Li
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible. Most detectors consider each 3D object as an independent training target, inevitably resulting in a lack of useful information for occluded samples. To this end, we propose a novel method to improve the monocular 3D object detection by considering the relationship of paired samples. This allows us to encode spatial constraints for partially-occluded objects from their adjacent neighbors. Specifically, the proposed detector computes uncertainty-aware predictions for object locations and 3D distances for the adjacent object pairs, which are subsequently jointly optimized by nonlinear least squares. Finally, the one-stage uncertainty-aware prediction structure and the post-optimization module are dedicatedly integrated for ensuring the run-time efficiency. Experiments demonstrate that our method yields the best performance on KITTI 3D detection benchmark, by outperforming state-of-the-art competitors by wide margins, especially for the hard samples.
