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.
@article{arxiv.2303.17895,
title = {EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection},
author = {Haotian Hu and Fanyi Wang and Jingwen Su and Yaonong Wang and Laifeng Hu and Weiye Fang and Jingwei Xu and Zhiwang Zhang},
journal= {arXiv preprint arXiv:2303.17895},
year = {2023}
}