从稀疏点云重建带符号距离函数的曲面，用可逆参数化+网格变形提升精度
Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation
- 通过可逆曲面参数化将局部点块映射到全局表面
- 在合成与真实扫描数据上优于现有最佳方法
- 适合需要高精度曲面重建的研究者
从稀疏点云中推断带符号距离函数（SDF）仍是表面重建中的挑战，核心在于稀疏点云缺乏详细几何信息，难以学习连续场。为此，我们提出一种新方法，通过端到端学习动态变形网络预测SDF。为从稀疏点云参数化连续曲面，我们设计了可逆曲面参数化（BSP），从局部片段学习全局形状。具体而言，构建从参数域到3D局部块的双射映射，将局部块整合为全局表面。同时，引入网格变形优化（GDO）改进网格点的形变，进一步精炼参数化曲面。在合成与真实扫描数据集上的实验表明，该方法显著优于当前最先进的方法。
原文摘要 · Abstract (English)
Inferring signed distance functions (SDFs) from sparse point clouds remains a challenge in surface reconstruction. The key lies in the lack of detailed geometric information in sparse point clouds, which is essential for learning a continuous field. To resolve this issue, we present a novel approach that learns a dynamic deformation network to predict SDFs in an end-to-end manner. To parameterize a continuous surface from sparse points, we propose a bijective surface parameterization (BSP) that learns the global shape from local patches. Specifically, we construct a bijective mapping for sparse points from the parametric domain to 3D local patches, integrating patches into the global surface. Meanwhile, we introduce grid deformation optimization (GDO) into the surface approximation to optimize the deformation of grid points and further refine the parametric surfaces. Experimental results on synthetic and real scanned datasets demonstrate that our method significantly outperforms the current state-of-the-art methods. Project page: https://takeshie.github.io/Bijective-SDF
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