Search Results for author:
Found 6 papers, 3 papers with code
Small Steps and Level Sets: Fitting Neural Surface Models with Point Guidance
We circumvent this problem by introducing guiding points and use them to steer the optimization towards the true shape via small incremental changes for which the loss formulation has a good descent direction.
Octree Guided Unoriented Surface Reconstruction
We propose a two-step approach, OG-INR, where we (1) construct a discrete octree and label what is inside and outside (2) optimize for a continuous and high-fidelity shape using an INR that is initially guided by the octree's labelling.
Exploiting Problem Structure in Deep Declarative Networks: Two Case Studies
Deep declarative networks and other recent related works have shown how to differentiate the solution map of a (continuous) parametrized optimization problem, opening up the possibility of embedding mathematical optimization problems into end-to-end learnable models.
DiGS: Divergence Guided Shape Implicit Neural Representation for Unoriented Point Clouds
In this paper, we propose a divergence guided shape representation learning approach that does not require normal vectors as input.
DiGS : Divergence guided shape implicit neural representation for unoriented point clouds
In this paper, we propose a divergence guided shape representation learning approach that does not require normal vectors as input.
Computer Assisted Composition in Continuous Time
We address the problem of combining sequence models of symbolic music with user defined constraints.
