BEVScope: Enhancing Self-Supervised Depth Estimation Leveraging
  Bird's-Eye-View in Dynamic Scenarios
Yucheng Mao Ruowen Zhao Tianbao Zhang Hang Zhao
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.