DPS-Net: Direction-Aware Pseudo-Stereo Network for Accurate Road Surface Reconstruction

The geometry of road surfaces plays a critical role in the performance of autonomous driving systems. Consequently, achieving accurate and efficient road surface reconstruction (RSR) is of paramount importance. However, due to the inherent effects of perspective projection, distant regions often exhibit geometric distortions and a long-tailed distribution, which pose significant challenges to existing reconstruction methods. To address these issues, we propose a novel framework, termed Direction-aware Pseudo-Stereo Road Reconstruction Network (DPS-Net), which incorporates two lightweight and plug-and-play modules: Direction-Aware Feature Enhancement (DFE) module and Pseudo-Stereo Fusion (PSF) module. The DFE module is designed to enhance the perception of sparse and geometry-invariant features by integrating directional context, while the PSF module captures global dependencies across spatial and channel dimensions through pseudo-stereo fusion. Both modules are constructed with an emphasis on maintaining low computational complexity. We conducted extensive experiments on the public RSRD dataset to evaluate the effectiveness and superiority of our proposed method. The code is available at https://github.com/yidanyi/DPS-Net.