Neighborhood-based neural implicit reconstruction from point clouds

Neural implicit reconstruction is emerging as a promising approach to constructing 3D geometry from point clouds due to its ability to model geometry with complicated topology and unrestricted resolution. Current methods in this category usually deliver smooth and good quality results,but suffer from defective details and generalization issues. The major reason is that these methods use either a global code or interpolated feature on 3D grids of limited resolution to estimate implicit surface,therefore may cause distortion in feature discretization. This paper presents a neighborhood-aware neural implicit reconstruction framework that consists of an encoder network,a feature aggregation module,and a decoder network to learn implicit surface. The method can easily incorporate an off-the-shelf 3D point-based or volume-based neural network as an encoder. At the heart of our framework is the aggregation module that fuses the learnt contextual features on neighbor inputs so that the method can directly exploit local features of neighboring inputs for geometry detail recovery as well as cross-domain generalization. Experimental results demonstrate that our method significantly outperforms the state-of-the-art methods (about 4.0 points IoU improvements in ShapeNet dataset and 9.0 points IoU improvements in DFAUST dataset). Furthermore,our method preserves finer shape details and can be successfully transferred to a novel category without fine-tuning.