PGDepth: Roadside Long-Range Depth Estimation Guided by Priori Geometric Information

Long-range depth is crucial for roadside perception, which helps vehicles detect potential threats earlier, respond promptly, and avoid collisions. However, a notable challenge with existing roadside perception methods is their difficulty in accurately perceiving objects at long-range depth. To this end, we propose a Priori Geometric-Guided long-range Depth estimation framework, named PGDepth. First, inspired by the human ability to perceive depth by referencing objects, we utilize priori Geometric as reference information for road objects, assigning each pixel a predefined depth range. Second, a coarse-to-fine approach is introduced to continuously refine the accuracy of depth distribution for pixels. Furthermore, we propose a new loss function to effectively supervise the depth distributions of road objects. Extensive experimental results on the DAIR dataset demonstrate that the proposed method surpasses previous state-of-the-art competitors.