Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images

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We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views. Previous work on learning shape reconstruction from multiple views uses discrete representations such as point clouds or voxels, while continuous surface generation approaches lack multi-view consistency. We address these issues by designing neural networks capable of generating high-quality parametric 3D surfaces which are also consistent between views. Furthermore, the generated 3D surfaces preserve accurate image pixel to 3D surface point correspondences, allowing us to lift texture information to reconstruct shapes with rich geometry and appearance. Our method is supervised and trained on a public dataset of shapes from common object categories. Quantitative results indicate that our method significantly outperforms previous work, while qualitative results demonstrate the high quality of our reconstructions.
Publisher
Springer Science and Business Media Deutschland GmbH
Issue Date
2020-08
Language
English
Citation

16th European Conference on Computer Vision, ECCV 2020, pp.121 - 138

ISSN
0302-9743
DOI
10.1007/978-3-030-58523-5_8
URI
http://hdl.handle.net/10203/281010
Appears in Collection
CS-Conference Papers(학술회의논문)
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