Winding Number-Guided Edge-Preserving Implicit Neural Representation of CAD Surfaces

Abstract

Implicit surface representations have emerged as a powerful tool for the task of 3D reconstruction due to their excellent performance. Yet, when the normal information cannot be available, the previous methods often lead to unsatisfactory reconstruction results, even failure. To this end, we propose a winding number—guided implicit surface reconstruction method, which mainly consists of a winding number—guided regularizer and a dynamic edge sampling strategy. Among them, the winding number-guided regularizer can effectively constrain the global normal consistency of the input raw data, as well as improve the unsatisfactory implicit surface reconstruction result caused by the unavailability of normal information. Meanwhile, in order to reduce the excessive smoothing at sharp edges of implicit surface, we proposed a dynamic edge sampling strategy for sampling near the sharp edge regions of 3D shape, which can effectively avoid the regularizer from smoothing all regions. Finally, we combine them with a simple data term for robust implicit surface reconstruction. Compared with the state-of-the-art methods, experimental results show that our method significantly improves the quality of 3D reconstruction results. In addition, since the winding number-guided regularizer effectively constraints the globally consistent normal of the input 3D raw data, our method can also receive an additional gift, namely the globally consistent normal estimation results of 3D raw data.