Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Shi, Kanle"'
Unsigned distance functions (UDFs) have been a vital representation for open surfaces. With different differentiable renderers, current methods are able to train neural networks to infer a UDF by minimizing the rendering errors on the UDF to the mult
Externí odkaz:
http://arxiv.org/abs/2407.16396
Diffusion models have shown remarkable results for image generation, editing and inpainting. Recent works explore diffusion models for 3D shape generation with neural implicit functions, i.e., signed distance function and occupancy function. However,
Externí odkaz:
http://arxiv.org/abs/2404.06851
Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal supervision. However, normal supervision in benchmarks comes from synthet
Externí odkaz:
http://arxiv.org/abs/2311.00389
We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excellent gradient approximation properties for the underlying geometry of th
Externí odkaz:
http://arxiv.org/abs/2309.09211
We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods
Externí odkaz:
http://arxiv.org/abs/2305.05873
Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more
Externí odkaz:
http://arxiv.org/abs/2208.06821
We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec3f040a39d54eae09fdb1a65617cd17
http://arxiv.org/abs/2305.05873
http://arxiv.org/abs/2305.05873
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Publikováno v:
Visual Computing for Industry, Biomedicine & Art; 12/3/2021, Vol. 4 Issue 1, p1-13, 13p
Akademický článek
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