We term our framework as Sign-Agnostic Implicit Learning of Surface Self-Similarities (SAIL-S3). With a global post-optimization of local sign flipping, SAIL-S3 is able to directly model raw, un-oriented point clouds and reconstruct high-quality object surfaces. Experiments show its superiority over existing methods.
Dec 14, 2020 · Abstract:Shape modeling and reconstruction from raw point clouds of objects stand as a fundamental challenge in vision and graphics research ...
We propose a novel method of SAIL-S3 for surface modeling and reconstruction from raw, un-oriented point clouds. The method learns self-adaptive shape priors by ...
Dec 14, 2020 · Shape modeling and reconstruction from raw point clouds of objects stand as a fundamental challenge in vision and graphics research.
Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Clouds. – Supplemental Material –. A. Least ...
... Some methods were proposed to learn implicit functions from point clouds without 3D ground truth. ... One issue here is that they usually assume the point ...
Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Clouds.
This paper introduces Sign Agnostic Learning (SAL), a deep learning approach for learning implicit shape representations directly from raw, ...
Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape ...
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Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Clouds. W. Zhao, J. Lei, Y. Wen, J. Zhang, ...
Aug 27, 2021 · Sign-agnostic implicit learning of surface self- similarities for shape modeling and reconstruction from raw point clouds. In CVPR, 2021.