Liu, W., Li, J., Chen, X., Hou, F., Xin, S., Wang, X., Wu, Z., Qian, C., & He, Y. (2024). Diffusing Winding Gradients (DWG): A Parallel and Scalable Method for 3D Reconstruction from Unoriented Point Clouds. arXiv preprint arXiv:2405.13839v2.
This paper introduces a novel method, Diffusing Winding Gradients (DWG), for reconstructing watertight 3D surfaces from unoriented point clouds, aiming to address the limitations of existing methods in terms of scalability and runtime performance.
DWG leverages the alignment between the gradients of the generalized winding number (GWN) field and globally consistent normals to orient points effectively. It iteratively updates point normals by diffusing the normalized gradient of the GWN field associated with the current normals. The algorithm utilizes an octree for space discretization, a kd-tree for efficient nearest neighbor searching, and a screened variant of GWN for enhanced robustness against noise and outliers.
DWG presents a significant advancement in 3D reconstruction from unoriented point clouds, offering a fast, robust, and scalable solution that surpasses existing methods in terms of speed and efficiency. Its solver-free implementation and parallel architecture make it particularly suitable for large-scale models and GPU acceleration.
DWG's high performance and reliability push the boundaries of what is achievable in 3D surface reconstruction, offering a valuable tool for various applications in computer graphics, 3D vision, and related fields.
While DWG demonstrates superior performance, future research could explore further optimizations for specific applications, such as handling extremely noisy point clouds or incorporating adaptive screening parameters for improved detail preservation.
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