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119 results on '"Jean-Sébastien Franco"'

1. Multi-Modal Data Correspondence for the 4D Analysis of the Spine with Adolescent Idiopathic Scoliosis

Author

Nicolas Comte, Sergi Pujades, Aurélien Courvoisier, Olivier Daniel, Jean-Sébastien Franco, François Faure, and Edmond Boyer

Subjects
motion capture, idiopathic scoliosis, kinematics, spine, subject-specific modeling, Technology, Biology (General), QH301-705.5
Abstract

Adolescent idiopathic scoliosis is a three-dimensional spinal deformity that evolves during adolescence. Combined with static 3D X-ray acquisitions, novel approaches using motion capture allow for the analysis of the patient dynamics. However, as of today, they cannot provide an internal analysis of the spine in motion. In this study, we investigated the use of personalized kinematic avatars, created with observations of the outer (skin) and internal shape (3D spine) to infer the actual anatomic dynamics of the spine when driven by motion capture markers. Towards that end, we propose an approach to create a subject-specific digital twin from multi-modal data, namely, a surface scan of the back of the patient and a reconstruction of the 3D spine (EOS). We use radio-opaque markers to register the inner and outer observations. With respect to the previous work, our method does not rely on a precise palpation for the placement of the markers. We present the preliminary results on two cases, for which we acquired a second biplanar X-ray in a bending position. Our model can infer the spine motion from mocap markers with an accuracy below 1 cm on each anatomical axis and near 5 degrees in orientations.

Published
2023
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4. Multicamera Real-Time 3D Modeling for Telepresence and Remote Collaboration

Author

Benjamin Petit, Jean-Denis Lesage, Clément Menier, Jérémie Allard, Jean-Sébastien Franco, Bruno Raffin, Edmond Boyer, and François Faure

Subjects
Telecommunication, TK5101-6720
Abstract

We present a multicamera real-time 3D modeling system that aims at enabling new immersive and interactive environments. This system, called Grimage, allows to retrieve in real-time a 3D mesh of the observed scene as well as the associated textures. This information enables a strong visual presence of the user into virtual worlds. The 3D shape information is also used to compute collisions and reaction forces with virtual objects, enforcing the mechanical presence of the user in the virtual world. The innovation is a fully integrated system with both immersive and interactive capabilities. It embeds a parallel version of the EPVH modeling algorithm inside a distributed vision pipeline. It also adopts the hierarchical component approach of the FlowVR middleware to enforce software modularity and enable distributed executions. Results show high refresh rates and low latencies obtained by taking advantage of the I/O and computing resources of PC clusters. The applications we have developed demonstrate the quality of the visual and mechanical presence with a single platform and with a dual platform that allows telecollaboration.

Published
2010
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34. Mesh Denoising With Facet Graph Convolutions

Author

Edmond Boyer, Matthieu Armando, Jean-Sébastien Franco, Capture and Analysis of Shapes in Motion (MORPHEO), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Institut National de Recherche en Informatique et en Automatique (Inria), MSR-Inria, and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Computer science, Noise reduction, Mesh denoising, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Geometric deep learning, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], Computer Graphics and Computer-Aided Design, Graph, Vertex (geometry), Convolution, Feature preserving, Normal filtering, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Polygon mesh, Computer Vision and Pattern Recognition, Algorithm, Graph convolutions, Software, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

code available at the following address:https://gitlab.inria.fr/marmando/deep-mesh-denoizing; International audience; We examine the problem of mesh denoising, which consists of removing noise from corrupted 3D meshes while preserving existing geometric features. Most mesh denoising methods require a lot of mesh-specific parameter fine-tuning, to account for specific features and noise types. In recent years, data-driven methods have demonstrated their robustness and effectiveness with respect to noise and feature properties on a wide variety of geometry and image problems. Most existing mesh denoising methods still use hand-crafted features, and locally denoise facets rather than examine the mesh globally. In this work, we propose the use of a fully end-to-end learning strategy based on graph convolutions, where meaningful features are learned directly by our network. It operates on a graph of facets, directly on the existing topology of the mesh, without resampling, and follows a multi-scale design to extract geometric features at different resolution levels. Similar to most recent pipelines, given a noisy mesh, we first denoise face normals with our novel approach, then update vertex positions accordingly. Our method performs significantly better than the current state-of-the-art learning-based methods. Additionally, we show that it can be trained on noisy data, without explicit correspondence between noisy and ground-truth facets. We also propose a multi-scale denoising strategy, better suited to correct noise with a low spatial frequency.

Published
2022
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