Depth-powered Moving-obstacle Segmentation Under Bird-eye-view for Autonomous Driving

Sensing the moving obstacles accurately under birdeye view (BEV) is the foundation for reliable autonomous driving, providing straightforward information for the downstream tasks. However, accurately segmenting moving obstacles only through monocular camera views is extremely difficult due to the lack of depth information. It can easily generate the projected depth information from point clouds, but its sparsity provides incomplete depth information. Therefore, in this paper, we propose a dense depth-powered framework, dubbed DPMoSeg, to generate dense moving-obstacle segmentation observations under BEV space. To better represent the depth prediction, we design a sparse-dense attention module to fully combine the knowledge across non- homogeneous and homogeneous representations. The experimental results demonstrate the effectiveness and superiority of our proposed framework.