1 code implementation • CVPR 2023 • Chamin Hewa Koneputugodage, Yizhak Ben-Shabat, Stephen Gould
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.
no code implementations • 24 Feb 2022 • Stephen Gould, Dylan Campbell, Itzik Ben-Shabat, Chamin Hewa Koneputugodage, Zhiwei Xu
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.
1 code implementation • CVPR 2022 • Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Stephen Gould
In this paper, we propose a divergence guided shape representation learning approach that does not require normal vectors as input.
1 code implementation • 21 Jun 2021 • Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Stephen Gould
In this paper, we propose a divergence guided shape representation learning approach that does not require normal vectors as input.
no code implementations • 10 Sep 2019 • Chamin Hewa Koneputugodage, Rhys Healy, Sean Lamont, Ian Mallett, Matt Brown, Matt Walters, Ushini Attanayake, Libo Zhang, Roger T. Dean, Alexander Hunter, Charles Gretton, Christian Walder
We address the problem of combining sequence models of symbolic music with user defined constraints.