Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement

• URL: http://arxiv.org/abs/2412.19165v1
• Date: Thu, 26 Dec 2024 10:51:50 GMT
• Title: Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement
• Authors: Qiude Zhang, Chunyu Lin, Zhijie Shen, Nie Lang, Yao Zhao,
• Abstract summary: Monocular 3D object detection is challenging due to the lack of accurate depth. Existing depth-assisted solutions still exhibit inferior performance. We propose a novel depth-adapted monocular 3D object detection network.
• Score: 44.4805861813093
• License:
• Abstract: Monocular 3D object detection is challenging due to the lack of accurate depth. However, existing depth-assisted solutions still exhibit inferior performance, whose reason is universally acknowledged as the unsatisfactory accuracy of monocular depth estimation models. In this paper, we revisit monocular 3D object detection from the depth perspective and formulate an additional issue as the limited 3D structure-aware capability of existing depth representations (\textit{e.g.}, depth one-hot encoding or depth distribution). To address this issue, we propose a novel depth-adapted monocular 3D object detection network, termed \textbf{RD3D}, that mainly comprises a Scene-Level Depth Retargeting (SDR) module and an Instance-Level Spatial Refinement (ISR) module. The former incorporates the scene-level perception of 3D structures, retargeting traditional depth representations to a new formulation: \textbf{Depth Thickness Field}. The latter refines the voxel spatial representation with the guidance of instances, eliminating the ambiguity of 3D occupation and thus improving detection accuracy. Extensive experiments on the KITTI and Waymo datasets demonstrate our superiority to existing state-of-the-art (SoTA) methods and the universality when equipped with different depth estimation models. The code will be available.

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