Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering Article Swipe

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Baixin Xu , Jiangbei Hu , Fei Hou , Kwan-Yee Lin , Wayne Wu , Qián Chen , Ying He ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2310.05524

The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces. This paper introduces a novel neural algorithm for parameterizing neural implicit surfaces to simple parametric domains like spheres and polycubes. Our method allows users to specify the number of cubes in the parametric domain, learning a configuration that closely resembles the target 3D object's geometry. It computes bi-directional deformation between the object and the domain using a forward mapping from the object's zero level set and an inverse deformation for backward mapping. We ensure nearly bijective mapping with a cycle loss and optimize deformation smoothness. The parameterization quality, assessed by angle and area distortions, is guaranteed using a Laplacian regularizer and an optimized learned parametric domain. Our framework integrates with existing neural rendering pipelines, using multi-view images of a single object or multiple objects of similar geometries to reconstruct 3D geometry and compute texture maps automatically, eliminating the need for any prior information. We demonstrate the method's effectiveness on images of human heads and man-made objects.

Related Topics

Computer Science

Rendering (Computer Graphics)

Texture Mapping

Polygon Mesh

Artificial Intelligence

Computer Vision

Visualization (Graphics)

Computer Graphics

Mathematics

Statistics

Concepts

Computer science Rendering (computer graphics) Texture mapping Artificial neural network Polygon mesh Parametric statistics Artificial intelligence Leverage (statistics) Computer vision Visualization Computer graphics (images) Texture atlas Image texture Mathematics Image processing Image (mathematics) Statistics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2310.05524
PDF: https://arxiv.org/pdf/2310.05524
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4387560118

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DOI: https://doi.org/10.48550/arxiv.2310.05524

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Title: Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-10-09

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Authors: Baixin Xu, Jiangbei Hu, Fei Hou, Kwan-Yee Lin, Wayne Wu, Qián Chen, Ying He

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Landing page: https://arxiv.org/abs/2310.05524

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Concepts: Computer science, Rendering (computer graphics), Texture mapping, Artificial neural network, Polygon mesh, Parametric statistics, Artificial intelligence, Leverage (statistics), Computer vision, Visualization, Computer graphics (images), Texture atlas, Image texture, Mathematics, Image processing, Image (mathematics), Statistics

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abstract_inverted_index.rendering	4, 135
abstract_inverted_index.resembles	62
abstract_inverted_index.surfaces.	22
abstract_inverted_index.geometries	149
abstract_inverted_index.guaranteed	118
abstract_inverted_index.integrates	131
abstract_inverted_index.introduces	25
abstract_inverted_index.multi-view	138
abstract_inverted_index.parametric	37, 55, 127
abstract_inverted_index.pipelines,	136
abstract_inverted_index.polycubes.	42
abstract_inverted_index.techniques	10
abstract_inverted_index.deformation	71, 91, 106
abstract_inverted_index.demonstrate	167
abstract_inverted_index.eliminating	159
abstract_inverted_index.reconstruct	151
abstract_inverted_index.regularizer	122
abstract_inverted_index.represented	18
abstract_inverted_index.smoothness.	107
abstract_inverted_index.advancements	1
abstract_inverted_index.distortions,	116
abstract_inverted_index.information.	165
abstract_inverted_index.configuration	59
abstract_inverted_index.effectiveness	170
abstract_inverted_index.automatically,	158
abstract_inverted_index.bi-directional	70
abstract_inverted_index.parameterizing	31
abstract_inverted_index.parameterization	109
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	7
citation_normalized_percentile