Edge-aware Neural Implicit Surface Reconstruction

Recently, neural implicit 3D reconstruction in indoor scenarios has achieved impressive performance. Utilizing the volume rendering method and neural implicit representation to learn 3D scenes, such per-scene optimization methods could reconstruct pretty complete models but also suffer from missing details and overly-smoothed reconstructions. In this paper, we propose a novel edge-aware neural implicit surface reconstruction method, named Ea-NeuS, to learn high-quality 3D models with fine details. Specifically, we use the edge of objects to locate the important areas, and propose a simple yet effective edge-guided ray-sampling strategy to learn the 3D models. The aforementioned edge information further guides the normal prior supervision, which helps reduce inaccurate optimization in detailed regions. We additionally use the visibility-aware sparse points to pilot the 3D points sampling along the rays and perform explicit supervision. As a result, our method achieves superior performance compared with existing methods on various scenes.