Jun 20, 2021 · We exploit this to define DeepMesh - an end-to-end differentiable mesh representation that can vary its topology. We validate our theoretical ...
The first one emulates iso-surface extraction with deep neural networks, while the second one avoids the need for mesh representations by formulating objectives ...
This lets us extract the explicit surface using a non-differentiable algorithm, such as. Marching Cubes, and then perform the backward pass through the.
We have introduced DeepMesh , a new approach to extracting 3D surface meshes from continuous deep implicit fields while preserving end-to-end differentiability.
Sep 7, 2024 · Our method is order of magnitude faster than meshing a dense point cloud, and more accurate than inflating open surfaces. Moreover, we make our ...
Differentiable iso-surface extraction. We show how to backpropagate gradient information from mesh vertices to latent vector while modifying surface mesh ...
In short, our core contribution is a theoretically well-grounded technique for differentiating through iso-surface extraction. This enables us to harness the ...
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Oct 3, 2024 · Our key insight is that by reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3D ...
Apr 22, 2024 · They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D euclidean grid, resulting in a learnable ...
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What is Isosurface extraction?
By reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3D location of surface samples with ...