Depth estimation is a cornerstone of perception in autonomous driving and robotic systems. The considerable cost and relatively sparse data acquisition of LiDAR systems have led to the exploration of cost-effective alternatives, notably, self-supervised depth estimation. Nevertheless, current self-supervised depth estimation methods grapple with several limitations: (1) the failure to adequately leverage informative multi-camera views. (2) the limited capacity to handle dynamic objects effectively. To address these challenges, we present BEVScope, an innovative approach to self-supervised depth estimation that harnesses Bird's-Eye-View (BEV) features. Concurrently, we propose an adaptive loss function, specifically designed to mitigate the complexities associated with moving objects. Empirical evaluations conducted on the Nuscenes dataset validate our approach, demonstrating competitive performance. Code will be released at https://github.com/myc634/BEVScope.

翻译：深度估计是自动驾驶与机器人系统中感知能力的基础。由于激光雷达系统成本高昂且数据采集相对稀疏，促使研究人员探索更具成本效益的替代方案，其中自监督深度估计尤为突出。然而，当前自监督深度估计方法存在若干局限：（1）未能充分利用多摄像头视角中的丰富信息；（2）有效处理动态目标的能力有限。为解决上述挑战，我们提出BEVScope——一种创新性的自监督深度估计方法，该方法通过利用鸟瞰图（BEV）特征来提升性能。同时，我们设计了一种自适应损失函数，专门用于缓解运动目标带来的复杂性。在Nuscenes数据集上的实验评估验证了该方法的有效性，并展现出具有竞争力的性能。代码将发布于https://github.com/myc634/BEVScope。