In recent years, great progress has been made in the Lift-Splat-Shot-based (LSS-based) 3D object detection method, which converts features of 2D camera view and 3D lidar view to Bird's-Eye-View (BEV) for feature fusion. However, inaccurate depth estimation (e.g. the 'depth jump' problem) is an obstacle to develop LSS-based methods. To alleviate the 'depth jump' problem, we proposed Edge-Aware Bird's-Eye-View (EA-BEV) projector. By coupling proposed edge-aware depth fusion module and depth estimate module, the proposed EA-BEV projector solves the problem and enforces refined supervision on depth. Besides, we propose sparse depth supervision and gradient edge depth supervision, for constraining learning on global depth and local marginal depth information. Our EA-BEV projector is a plug-and-play module for any LSS-based 3D object detection models, and effectively improves the baseline performance. We demonstrate the effectiveness on the nuScenes benchmark. On the nuScenes 3D object detection validation dataset, our proposed EA-BEV projector can boost several state-of-the-art LLS-based baselines on nuScenes 3D object detection benchmark and nuScenes BEV map segmentation benchmark with negligible increment of inference time.

翻译：近年来，基于Lift-Splat-Shot（LSS）的三维目标检测方法取得了重大进展，该方法将二维相机视角与三维激光雷达视角的特征转换为鸟瞰图（BEV）进行特征融合。然而，不准确的深度估计（如"深度跳跃"问题）成为制约LSS方法发展的瓶颈。为缓解该问题，我们提出了边感知鸟瞰投影器（EA-BEV）。通过耦合所提出的边感知深度融合模块与深度估计模块，EA-BEV投影器解决了该问题，并对深度施加了精细化监督。此外，我们提出稀疏深度监督与梯度边缘深度监督，用于约束全局深度与局部边缘深度信息的学习。EA-BEV投影器作为即插即用模块，可适用于任意基于LSS的三维目标检测模型，并有效提升基线性能。我们在nuScenes基准数据集上验证了其有效性。在nuScenes三维目标检测验证集上，所提出的EA-BEV投影器能够以可忽略的推理时间增量，显著提升多个基于LSS的最新基线模型在nuScenes三维目标检测基准与BEV地图分割基准上的性能。