论文标题
Geoudf：通过几何引导的距离表示从3D点云中重建的表面重建
GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation
论文作者
论文摘要
我们提出了一种基于学习的方法，即geoudf，以解决从稀疏点云中重建离散表面的长期和挑战性的问题。要具体，我们提出了针对UDF的几何学引导的学习方法及其梯度估计，以明确地将Query Point的距离列为可学习的距离阶层的距离，从而在距离上进行了差异的距离，从而构成了距离的距离，从而构成了狭窄的距离。此外，我们通过明确学习每个点的二次多项式来对输入点云的局部几何结构进行建模。这不仅有助于提高输入稀疏点云，而且自然诱导了无定向的正常，这进一步增强了UDF估计。最后，为了从预测的UDF中提取三角形网络，我们提出了一个定制的基于边缘的行进立方体模块。我们进行了广泛的实验和消融研究，以证明我们方法在重建准确性，效率和一般性方面的显着优势而不是最先进的方法。源代码可在https://github.com/rsy6318/geoudf上公开获得。
We present a learning-based method, namely GeoUDF,to tackle the long-standing and challenging problem of reconstructing a discrete surface from a sparse point cloud.To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation that explicitly formulates the unsigned distance of a query point as the learnable affine averaging of its distances to the tangent planes of neighboring points on the surface. Besides,we model the local geometric structure of the input point clouds by explicitly learning a quadratic polynomial for each point. This not only facilitates upsampling the input sparse point cloud but also naturally induces unoriented normal, which further augments UDF estimation. Finally, to extract triangle meshes from the predicted UDF we propose a customized edge-based marching cube module. We conduct extensive experiments and ablation studies to demonstrate the significant advantages of our method over state-of-the-art methods in terms of reconstruction accuracy, efficiency, and generality. The source code is publicly available at https://github.com/rsy6318/GeoUDF.
