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MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli · Artem Lukoianov · Stephan Richter · Benoit Guillard · Timur Bagautdinov · Pierre Baque · Pascal Fua

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #348

Geometric Deep Learning has recently made striking progress with the advent of continuous Deep Implicit Fields. They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable parameterization that is not limited in resolution.

Unfortunately, these methods are often not suitable for applications that require an explicit mesh-based surface representation because converting an implicit field to such a representation relies on the Marching Cubes algorithm, which cannot be differentiated with respect to the underlying implicit field.

In this work, we remove this limitation and introduce a differentiable way to produce explicit surface mesh representations from Deep Signed Distance Functions. Our key insight is that by reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3D location of surface samples with respect to the underlying deep implicit field. We exploit this to define MeshSDF, an end-to-end differentiable mesh representation which can vary its topology.

We use two different applications to validate our theoretical insight: Single-View Reconstruction via Differentiable Rendering and Physically-Driven Shape Optimization. In both cases our differentiable parameterization gives us an edge over state-of-the-art algorithms.

Author Information

Edoardo Remelli (EPFL)
Artem Lukoianov (EPFL/NeuralConcept)

Hello! I am a passionate researcher in Computer Vision. Currently, I am getting my Masters's degree in Data Science EPFL, Switzerland, and working part-time as a Machine Learning Researcher, building a system for the fastest Computer Assisted Engineering.

Stephan Richter (Intel Labs)
Benoit Guillard (EPFL)
Timur Bagautdinov (Facebook)
Pierre Baque (Neural Concept SA)
Pascal Fua (EPFL, Switzerland)

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