BEV-GS: Feed-forward Gaussian Splatting in Bird’s-Eye-View for Road Reconstruction

Road surfaces serve as the only contact medium between ve-hicle wheels or robot feet and the physical world. Reconstructing the roadsurface is crucial for unmanned vehicles and mobile robots. Recent stud-ies on Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) haveachieved remarkable results in scene reconstruction. However, they typi-cally rely on multi-view image inputs and require prolonged optimizationtimes. In this paper, we propose BEV-GS, a real-time single-frame roadsurface reconstruction method based on feed-forward Gaussian splatting.BEV-GS consists of a prediction module and a rendering module. Theprediction module introduces separate geometry and texture networksfollowing the Bird’s-Eye-View (BEV) paradigm. Geometric and textureparameters are directly estimated from a single frame, avoiding per-sceneoptimization. In the rendering module, we utilize grid Gaussian for roadsurface representation and novel view synthesis, which better aligns withroad surface characteristics. Our method achieves state-of-the-art per-formance on the real-world dataset RSRD. The road elevation error isreduced to 1.73 cm, and the PSNR of novel view synthesis reaches 28.36dB. The prediction and rendering FPS are 26 and 2061, respectively,enabling high-accuracy and real-time applications. Our code is availableat https://github.com/IRMVLab/BEV-GS.