Web13 Jun 2024 · Learning Joint Surface Atlases. Theo Deprelle, Thibault Groueix, Noam Aigerman, Vladimir G. Kim, Mathieu Aubry. This paper describes new techniques for learning atlas-like representations of 3D surfaces, i.e. homeomorphic transformations from a 2D domain to surfaces. Compared to prior work, we propose two major contributions. WebThibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry; Proceedings of the European Conference on Computer Vision (ECCV), 2024, pp. 230-246 Abstract We present a new deep learning approach for matching deformable shapes by introducing Shape Deformation Networks which jointly encode 3D shapes and …
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WebWe propose a self-supervised approach to deep surface deformation. Given a pair of shapes, our algorithm directly predicts a parametric transformation from one shape to the other … WebThibault Groueix is a Research Engineer working on 3D deep learning. In this field, Thibault is interested in any topic, ranging from 3D deformations, human-pose estimation, symmetry detection, generative models, texturing, reconstruction and retrieval systems. half marathon photo frame
[1806.05228] 3D-CODED : 3D Correspondences by Deep …
WebarXiv.org e-Print archive Web24 Apr 2024 · Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to … Web21 Feb 2024 · Excited to finally share *Neural Jacobian Fields* - our SIGGRAPH 2024 paper on learning highly-accurate deformations and mappings of 3D meshes, in a triangulation-agnostic manner. bundaberg library catalogue