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Found 7 papers, 5 papers with code
RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering
Finding accurate correspondences among different views is the Achilles' heel of unsupervised Multi-View Stereo (MVS).
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers
We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach.
NPMs: Neural Parametric Models for 3D Deformable Shapes
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.
Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction
We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects.
Neural Non-Rigid Tracking
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
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
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.
