BurnDC: A Progressive Propagation Framework for Low Coverage Depth Completion

Abstract

The growing adoption of compact and cost-effective solid-state LiDARs has greatly advanced robotics. However, their inherently limited Field-of-View (FOV) hinders their application in tasks requiring wide-range depth perception. To overcome this limitation, we introduce the Low Coverage Depth Completion (LCDC) task, which aims to generate a full-scene depth map from a low coverage sparse depth map and a corresponding RGB image, and propose a tailored framework named BurnDC. BurnDC progressively expands the propagation frontier around reliable depth anchors via Progressive Depth Burn (PDB) and utilizes Weighted Ring Attention (WRA) to inject stable geometric context into boundary regions, achieving a controlled refinement and completion of the entire depth map. To evaluate LCDC, we construct a real-world solid-state LiDAR based benchmark, LC-TIERS, and simulate low coverage settings in NYUv2 and KITTI. Experimental results demonstrate that BurnDC significantly outperforms existing methods in low coverage scenarios, with an RMSE reduction of 10–20% over top competitors. Our work provides a promising solution for unlocking the full potential of solid-state LiDARs in various applications.