Search Results for author: Aljaž Božič

Found 7 papers, 5 papers with code

RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering

1 code implementation8 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).

Neural Rendering

NPMs: Neural Parametric Models for 3D Deformable Shapes

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.

Pose Transfer

Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction

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.

3D Reconstruction

Neural Non-Rigid Tracking

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.

Learning to Optimize Non-Rigid Tracking

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.

DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data

1 code implementation9 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.

3D Reconstruction

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