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Related Experiment Video

Updated: Jun 28, 2025

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

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DeepMesh: Differentiable Iso-Surface Extraction.

Benoit Guillard, Edoardo Remelli, Artem Lukoianov

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 22, 2024

Summary

This summary is machine-generated.

This study introduces DeepMesh, a novel method enabling differentiable 3D mesh generation from deep implicit fields. This breakthrough allows for end-to-end training of 3D shape representations, overcoming limitations of previous approaches.

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Area of Science:

Computer Vision
Geometric Deep Learning
3D Shape Representation

Background:

Continuous deep implicit fields offer high-resolution 3D surface modeling without grid limitations.
Current methods struggle with mesh conversion due to non-differentiable algorithms like Marching Cubes.

Purpose of the Study:

To develop a differentiable method for generating explicit surface meshes from deep implicit fields.
To enable end-to-end training for 3D shape generation and manipulation.

Main Methods:

Introduced a novel approach to differentiate 3D surface sample locations with respect to the implicit field.
Developed DeepMesh, an end-to-end differentiable mesh representation capable of topological variations.

Main Results:

Demonstrated a differentiable pipeline for converting implicit fields to explicit meshes.
Achieved state-of-the-art results in single-view 3D reconstruction, shape optimization, and full scene reconstruction.

Conclusions:

DeepMesh provides a significant advancement for integrating implicit field representations with explicit mesh-based pipelines.
The end-to-end differentiable parameterization offers a competitive edge in various 3D computer vision applications.