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C3DPO

swMATH ID: 42749
Software Authors: David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova, Andrea Vedaldi
Description: C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion. We propose C3DPO, a method for extracting 3D models of deformable objects from 2D keypoint annotations in unconstrained images. We do so by learning a deep network that reconstructs a 3D object from a single view at a time, accounting for partial occlusions, and explicitly factoring the effects of viewpoint changes and object deformations. In order to achieve this factorization, we introduce a novel regularization technique. We first show that the factorization is successful if, and only if, there exists a certain canonicalization function of the reconstructed shapes. Then, we learn the canonicalization function together with the reconstruction one, which constrains the result to be consistent. We demonstrate state-of-the-art reconstruction results for methods that do not use ground-truth 3D supervision for a number of benchmarks, including Up3D and PASCAL3D+. Source code has been made available at https://github.com/facebookresearch/c3dpo_nrsfm
Homepage: https://arxiv.org/abs/1909.02533
Source Code:  https://github.com/facebookresearch/c3dpo_nrsfm
Related Software: Kaolin; 3D-R2N2; DensePose; TensorFlow; SynSin; Mitsuba; NeRF; DeepSDF; PointNet; Faster R-CNN; PIFu; Pixel2Mesh; LSUN; OpenDR; SMPL; SoftRas; ImageNet; ShapeNet; PyTorch; PyTorch3D
Cited in: 0 Publications