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.

翻译：近年来，基于提升-撒点-射击（LSS）的三维目标检测方法取得了显著进展。然而，深度估计不精确仍是限制纯相机及多模态三维目标检测模型精度的重要瓶颈，尤其在深度剧烈变化的区域（即"深度跳变"问题）。本文提出了一种新颖的边缘感知提升-撒点-射击（EA-LSS）框架。具体而言，我们提出了边缘感知深度融合（EADF）模块以缓解"深度跳变"问题，并设计了细粒度深度（FGD）模块以进一步强化对深度的精细化监督。该EA-LSS框架兼容所有基于LSS的三维目标检测模型，能以可忽略的推理时间增量有效提升模型性能。在nuScenes基准上的实验表明，EA-LSS在纯相机模型及多模态模型中均表现有效。值得注意的是，EA-LSS在nuScenes测试基准上取得了最优性能，其mAP和NDS分别达到76.5%和77.6%。