D$^2$GS: Dense Depth Regularization for LiDAR-free Urban Scene Reconstruction
Kejing Xia, Jidong Jia, Ke Jin, Yucai Bai, Li Sun, Dacheng Tao, Youjian Zhang
Recently, Gaussian Splatting (GS) has shown great potential for urban scenesreconstruction in the field of autonomous driving. However, current urban scenereconstruction methods often depend on multimodal sensors as inputs, i.e. LiDARand images. Though the geometry prior provided by LiDAR point cloud can largelymitigates ill-posednesses in reconstruction, acquiring such accurate LiDAR data isstill challenging in practice: i) precise spatiotemporal calibration between LiDARand other sensors is required, as they may not capture data simultaneously; ii)reprojection errors will arise due to spatial misalignment when LiDAR and camerasensors are mounted at distinct locations. To avoid the difficulty of acquiringaccurate LiDAR data, we propose D2GS, a LiDAR-free urban scene reconstructionframework. In this work, we obtain geometry priors that are as effective as thosefrom LiDAR, but are denser and more accurate. First, a dense point cloud isinitialized by predicting metric depths via a multi-view depth estimation method.This point cloud is then optimized by a progressive pruning strategy to improvethe global consistency. Second, we iteratively optimize the accuracy of predicteddepths and the rendering depths. Specifically,We leverage diffusion priors from adepth foundation model to enhance the depth maps rendered by Gaussians. In turn,the enhanced depths give better geometry constrain to the training of the Gaussians.Finally, We improve the accuracy of ground geometry by constraining the shapeand normal attributes of Gaussians within road regions. Extensive experiments onthe Waymo dataset demonstrate that our method consistently outperforms state-of-the-art methods, producing more accurate geometry even when compared withthose using ground truth LiDAR data.