EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection

• URL: http://arxiv.org/abs/2303.17895v4
• Date: Wed, 30 Aug 2023 02:10:53 GMT
• Title: EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection
• Authors: Haotian Hu, Fanyi Wang, Jingwen Su, Yaonong Wang, Laifeng Hu, Weiye Fang, Jingwei Xu, Zhiwang Zhang
• Abstract summary: We propose a novel Edge-aware Lift-splat-shot (EA-LSS) framework for 3D object detection. Our EA-LSS framework is compatible for any LSS-based 3D object detection models.
• Score: 9.289537252177048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, great progress has been made in the Lift-Splat-Shot-based (LSS-based) 3D object detection method. However, inaccurate depth estimation remains an important constraint to the accuracy of camera-only and multi-model 3D object detection models, especially in regions where the depth changes significantly (i.e., the "depth jump" problem). In this paper, we proposed a novel Edge-aware Lift-splat-shot (EA-LSS) framework. Specifically, edge-aware depth fusion (EADF) module is proposed to alleviate the "depth jump" problem and fine-grained depth (FGD) module to further enforce refined supervision on depth. Our EA-LSS framework is compatible for any LSS-based 3D object detection models, and effectively boosts their performances with negligible increment of inference time. Experiments on nuScenes benchmarks demonstrate that EA-LSS is effective in either camera-only or multi-model models. It is worth mentioning that EA-LSS achieved the state-of-the-art performance on nuScenes test benchmarks with mAP and NDS of 76.5% and 77.6%, respectively.

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