Mo, K;
Guerrero, P;
Yi, L;
Su, H;
Wonka, P;
Mitra, NJ;
Guibas, LJ;
(2020)
StructEdit: Learning Structural Shape Variations.
In:
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
(pp. pp. 8856-8865).
IEEE: Seattle, WA, USA.
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Abstract
Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and for many other applications in 3D content creation. The common approach of encoding shapes as points in a high-dimensional latent feature space suggests treating shape differences as vectors in that space. Instead, we treat shape differences as primary objects in their own right and propose to encode them in their own latent space. In a setting where the shapes themselves are encoded in terms of fine-grained part hierarchies, we demonstrate that a separate encoding of shape deltas or differences provides a principled way to deal with inhomogeneities in the shape space due to different combinatorial part structures, while also allowing for compactness in the representation, as well as edit abstraction and transfer. Our approach is based on a conditional variational autoencoder for encoding and decoding shape deltas, conditioned on a source shape. We demonstrate the effectiveness and robustness of our approach in multiple shape modification and generation tasks, and provide comparison and ablation studies on the PartNet dataset, one of the largest publicly available 3D datasets.
| Type: | Proceedings paper |
|---|---|
| Title: | StructEdit: Learning Structural Shape Variations |
| Event: | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CVPR42600.2020.00888 |
| Publisher version: | http://dx.doi.org/10.1109/CVPR42600.2020.00888 |
| 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: | Shape, Three-dimensional displays, Strain, Silicon, Geometry, Decoding, Encoding |
| UCL classification: | UCL 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/10117240 |
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