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In recent years, great progress has been made in the Lift-Splat-Shot-based(LSS-based) 3D object detection method. However, inaccurate depth estimationremains an important constraint to the accuracy of camera-only and multi-model3D object detection models, especially in regions where the depth changessignificantly (i.e., the "depth jump" problem). In this paper, we proposed anovel Edge-aware Lift-splat-shot (EA-LSS) framework. Specifically, edge-awaredepth fusion (EADF) module is proposed to alleviate the "depth jump" problemand fine-grained depth (FGD) module to further enforce refined supervision ondepth. Our EA-LSS framework is compatible for any LSS-based 3D object detectionmodels, and effectively boosts their performances with negligible increment ofinference time. Experiments on nuScenes benchmarks demonstrate that EA-LSS iseffective in either camera-only or multi-model models. It is worth mentioningthat EA-LSS achieved the state-of-the-art performance on nuScenes testbenchmarks with mAP and NDS of 76.5% and 77.6%, respectively.

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