FGS-NeRF: A fast glossy surface reconstruction method based on voxel and reflection directions

Abstract

Neural surface reconstruction technology has great potential for recovering 3D surfaces from multiview images. However, surface gloss can severely affect the reconstruction quality. Although existing methods address the issue of glossy surface reconstruction, achieving rapid reconstruction remains a challenge. While DVGO can achieve rapid scene geometry search, it tends to create numerous holes in glossy surfaces during the search process. To address this, we design a geometry search method based on SDF and reflection directions, employing a method called progressive voxel-MLP scaling to achieve accurate and efficient geometry searches for glossy scenes. To mitigate object edge artifacts caused by reflection directions, we use a simple loss function called sigmoid RGB loss, which helps reduce artifacts around objects during the early stages of training and promotes efficient surface convergence. In this work, we introduce the FGS-NeRF model, which uses a coarse-to-fine training method combined with reflection directions to achieve rapid reconstruction of glossy object surfaces based on voxel grids. The training time on a single RTX 4080 GPU is 20 min. Evaluations on the Shiny Blender and Smart Car datasets confirm that our model significantly improves the speed when compared with existing glossy object reconstruction methods while achieving accurate object surfaces. Code: https://github.com/yosugahhh/FGS-nerf.