OccupancyDETR: DETRを用いた密疎混合3D占有予測
OccupancyDETR: Using DETR for Mixed Dense-sparse 3D Occupancy Prediction
DETR風の物体検出と密疎混合3D占有デコーダを組み合わせ、前景と背景を分けて処理することで、効率的かつ高精度な3Dセマンティック占有予測を実現した。
著者: Yupeng Jia, Jie He, Runze Chen, Fang Zhao, Haiyong Luo
分類: cs.RO, cs.CV
原文アブストラクト
Visual-based 3D semantic occupancy perception is a key technology for robotics, including autonomous vehicles, offering an enhanced understanding of the environment by 3D. This approach, however, typically requires more computational resources than BEV or 2D methods. We propose a novel 3D semantic occupancy perception method, OccupancyDETR, which utilizes a DETR-like object detection, a mixed dense-sparse 3D occupancy decoder. Our approach distinguishes between foreground and background within a scene. Initially, foreground objects are detected using the DETR-like object detection. Subsequently, queries for both foreground and background objects are fed into the mixed dense-sparse 3D occupancy decoder, performing upsampling in dense and sparse methods, respectively. Finally, a MaskFormer is utilized to infer the semantics of the background voxels. Our approach strikes a balance between efficiency and accuracy, achieving faster inference times, lower resource consumption, and improved performance for small object detection. We demonstrate the effectiveness of our proposed method on the SemanticKITTI dataset, showcasing an mIoU of 14 and a processing speed of 10 FPS, thereby presenting a promising solution for real-time 3D semantic occupancy perception.
