1 code implementation • 8 Mar 2022 • Di Chang, Aljaž Božič, Tong Zhang, Qingsong Yan, Yingcong Chen, Sabine Süsstrunk, Matthias Nießner
Finding accurate correspondences among different views is the Achilles' heel of unsupervised Multi-View Stereo (MVS).
no code implementations • NeurIPS 2021 • Aljaž Božič, Pablo Palafox, Justus Thies, Angela Dai, Matthias Nießner
We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach.
1 code implementation • ICCV 2021 • Pablo Palafox, Aljaž Božič, Justus Thies, Matthias Nießner, Angela Dai
Crucially, once learned, our neural parametric models of shape and pose enable optimization over the learned spaces to fit to new observations, similar to the fitting of a traditional parametric model, e. g., SMPL.
1 code implementation • CVPR 2021 • Aljaž Božič, Pablo Palafox, Michael Zollhöfer, Justus Thies, Angela Dai, Matthias Nießner
We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects.
1 code implementation • NeurIPS 2020 • Aljaž Božič, Pablo Palafox, Michael Zollhöfer, Angela Dai, Justus Thies, Matthias Nießner
We introduce a novel, end-to-end learnable, differentiable non-rigid tracker that enables state-of-the-art non-rigid reconstruction by a learned robust optimization.
no code implementations • CVPR 2020 • Yang Li, Aljaž Božič, Tianwei Zhang, Yanli Ji, Tatsuya Harada, Matthias Nießner
One of the widespread solutions for non-rigid tracking has a nested-loop structure: with Gauss-Newton to minimize a tracking objective in the outer loop, and Preconditioned Conjugate Gradient (PCG) to solve a sparse linear system in the inner loop.
1 code implementation • 9 Dec 2019 • Aljaž Božič, Michael Zollhöfer, Christian Theobalt, Matthias Nießner
Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus.