用3D高斯点云实现无符号距离场重建，提升开放曲面精度与细节。
GaussianUDF: Inferring Unsigned Distance Functions through 3D Gaussian Splatting
- 通过拟合薄而平的2D高斯平面，实现隐式距离场学习。
- 在真实数据和基准测试中，重建结果更完整、更锐利、效率更高。
- 适合需要高精度三维重建的场景，如数字孪生与逆向建模。
从多视角图像重建开放曲面在数字化日常复杂物体中至关重要。现有方法常通过神经渲染验证外观一致性来学习无符号距离函数（UDFs）。然而，由于3D高斯点云（3DGS）是离散且显式的表示方式，难以学习连续的隐式UDF。为此，我们提出一种新方法，将2D高斯平面过拟合于表面，并利用自监督与基于梯度的推理，在表面近远区域共同监督无符号距离。为此，我们引入新的约束与策略，稳定优化过程并增强自监督可靠性，解决了UDF零水平集附近复杂梯度场带来的挑战。我们在多个主流基准与真实数据上进行数值与视觉对比，结果表明，本方法在重建精度、效率、完整性及边缘锐度方面均优于当前最优方法。
原文摘要 · Abstract (English)
Reconstructing open surfaces from multi-view images is vital in digitalizing complex objects in daily life. A widely used strategy is to learn unsigned distance functions (UDFs) by checking if their appearance conforms to the image observations through neural rendering. However, it is still hard to learn continuous and implicit UDF representations through 3D Gaussians splatting (3DGS) due to the discrete and explicit scene representation, i.e., 3D Gaussians. To resolve this issue, we propose a novel approach to bridge the gap between 3D Gaussians and UDFs. Our key idea is to overfit thin and flat 2D Gaussian planes on surfaces, and then, leverage the self-supervision and gradient-based inference to supervise unsigned distances in both near and far area to surfaces. To this end, we introduce novel constraints and strategies to constrain the learning of 2D Gaussians to pursue more stable optimization and more reliable self-supervision, addressing the challenges brought by complicated gradient field on or near the zero level set of UDFs. We report numerical and visual comparisons with the state-of-the-art on widely used benchmarks and real data to show our advantages in terms of accuracy, efficiency, completeness, and sharpness of reconstructed open surfaces with boundaries.
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