Browsing by Author "Tabia, Hedi"

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    Structure Learning for 3D Point Cloud Generation from Single RGB Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Charrada, Tarek Ben; Laga, Hamid; Tabia, Hedi; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    3D point clouds can represent complex 3D objects of arbitrary topologies and with fine-grained details. They are, however, hard to regress from images using convolutional neural networks, making tasks such as 3D reconstruction from monocular RGB images challenging. In fact, unlike images and volumetric grids, point clouds are unstructured and thus lack proper parameterization, which makes them difficult to process using convolutional operations. Existing point-based 3D reconstruction methods that tried to address this problem rely on complex end-to-end architectures with high computational costs. Instead, we propose in this paper a novel mechanism that decouples the 3D reconstruction problem from the structure (or parameterization) learning task, making the 3D reconstruction of objects of arbitrary topologies tractable and thus easier to learn. We achieve this using a novel Teacher-Student network where the Teacher learns to structure the point clouds. The Student then harnesses the knowledge learned by the Teacher to efficiently regress accurate 3D point clouds. We train the Teacher network using 3D ground-truth supervision and the Student network using the Teacher’'s annotations. Finally, we employ a novel refinement network to overcome the upper-bound performance that is set by the Teacher network. Our extensive experiments on ShapeNet and Pix3D benchmarks, and on in-the-wild images demonstrate that the proposed approach outperforms previous methods in terms of reconstruction accuracy and visual quality.
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    TopoNet: Topology Learning for 3D Reconstruction of Objects of Arbitrary Genus
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Ben Charrada, Tarek; Tabia, Hedi; Chetouani, Aladine; Laga, Hamid; Hauser, Helwig and Alliez, Pierre
    We propose a deep reinforcement learning‐based solution for the 3D reconstruction of objects of complex topologies from a single RGB image. We use a template‐based approach. However, unlike previous template‐based methods, which are limited to the reconstruction of 3D objects of fixed topology, our approach learns simultaneously the geometry and topology of the target 3D shape in the input image. To this end, we propose a neural network that learns to deform a template to fit the geometry of the target object. Our key contribution is a novel reinforcement learning framework that enables the network to also learn how to adjust, using pruning operations, the topology of the template to best fit the topology of the target object. We train the network in a supervised manner using a loss function that enforces smoothness and penalizes long edges in order to ensure high visual plausibility of the reconstructed 3D meshes. We evaluate the proposed approach on standard benchmarks such as ShapeNet, and in‐the‐wild using unseen real‐world images. We show that the proposed approach outperforms the state‐of‐the‐art in terms of the visual quality of the reconstructed 3D meshes, and also generalizes well to out‐of‐category images.