Publications by Edoardo Remelli

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In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pr ...

Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information. In this paper, we use an encoder/decoder architecture for the sketch to mesh translation. When integrated into a ...

We present a new algorithm for real-time hand tracking on commodity depth-sensing devices. Our method does not require a user-specific calibration session, but rather learns the geometry as the user performs live in front of the camera, thus enabling seaml ...

CNN-based volumetric methods that label individual voxels now dominate the field of biomedical segmentation. However, 3D surface representations are often required for proper analysis. They can be obtained by post-processing the labeled volumes which typic ...

Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations. However, these representations rarely preserve the Euclidean st ...

Aerodynamic shape optimization has many industrial applications. Existing methods, however, are so computationally demanding that typical engineering practices are to either simply try a limited number of hand-designed shapes or restrict oneself to shapes ...

Existing data-driven methods for draping garments over human bodies, despite being effective, cannot handle garments of arbitrary topology and are typically not end-to-end differentiable. To address these limitations, we propose an end-to-end differentiabl ...

Edoardo Remelli

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Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches

Online Generative Model Personalization for Hand Tracking

Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data

UCLID-Net: Single View Reconstruction in Object Space

Geodesic Convolutional Shape Optimization

DIG: Draping Implicit Garment over the Human Body

DeepMesh: Differentiable Iso-Surface Extraction

MeshSDF: Differentiable Iso-Surface Extraction