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

Lei, J; Sridhar, S; Guerrero, P; Sung, M; Mitra, N; Guibas, LJ; (2020) Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images. In: Computer Vision – ECCV 2020. 16th European Conference on Computer Vision. (pp. pp. 121-138). Springer Nature: Cham, Switzerland. Green open access

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Abstract

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.

Type: Proceedings paper
Title: Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images
ISBN-13: 978-3-030-58522-8
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-58523-5_8
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: 3D reconstruction, Multi-view, Single-view, Parametrization
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10122574
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