Efficient Implicit SDF and Color Reconstruction via Shared Feature Field

Recent advancements in neural implicit 3D representations have enabled simultaneous surface reconstruction and novel view synthesis using only 2D RGB images. However, these methods often struggle with textureless and minimally visible areas. In this study, we introduce a simple yet effective encoder-decoder framework that encodes positional and viewpoint coordinates into a shared feature field (SFF). This feature field is then decoded into an implicit signed distance field (SDF) and a color field. By employing a weight-sharing encoder, we enhance the joint optimization of the SDF and color field, enabling better utilization of the limited information in the scene. Additionally, we incorporate a periodic sine function as an activation function, eliminating the need for a positional encoding layer and significantly reducing rippling artifacts on surfaces. Empirical results demonstrate that our method more effectively reconstructs textureless and minimally visible surfaces, synthesizes higher-quality novel views, and achieves superior multi-view reconstruction with fewer input images.