A joint paper of Neural Concept SA and EPFL CVLab will be presented as a Spotlight presentation at Presentation for Neural Information Processing Systems Conference (NeurIPS) 2020 to reach a broader public to talk about MeshSDF that makes Deep SDF-based 3D generative models (VAEs) differentiable.

 

During the session, the team will introduce a differentiable way to produce explicit surface mesh representations from Deep Signed Distance Functions by removing the limitation of the Marching Cubes algorithm. The key insight is that by reasoning differentiate the 3D location of surface samples with respect to the underlying deep implicit field. The team exploit this to define MeshSDF, an end-to-end differentiable mesh representation which can vary its topology. They use two different applications to validate their theoretical insight: Single-View Reconstruction via Differentiable Rendering and Physically-Driven Shape Optimization.

 

The Neural Information Processing Systems Foundation is a non-profit corporation whose purpose is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical and theoretical aspects. The Conference on Neural Information Processing Systems is the main venue where the most groundbreaking scientific publications in machine learning are published every year, with more than 10,000 attendees.

 

NIPS 2020 is held Sun 6th December through Sat the 12th, 2020 at Virtual-only.

 

Read the full paper on : https://arxiv.org/abs/2006.03997

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