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Titlebook: Computer Vision – ECCV 2016; 14th European Confer Bastian Leibe,Jiri Matas,Max Welling Conference proceedings 2016 Springer International P

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31#
發(fā)表于 2025-3-26 23:55:38 | 只看該作者
32#
發(fā)表于 2025-3-27 03:07:00 | 只看該作者
Spatio-Temporally Consistent Correspondence for Dense Dynamic Scene Modelingcal image sequences. The obtained results for these two problems on multiple publicly available dynamic reconstruction datasets illustrate both the effectiveness and generality of our proposed approach.
33#
發(fā)表于 2025-3-27 05:50:23 | 只看該作者
34#
發(fā)表于 2025-3-27 12:19:17 | 只看該作者
Visualizing Image Priors to study various popular image models, and reveal interesting behaviors, which were not noticed in the past. We confirm our findings through denoising experiments. These validate that the structures we reveal as ‘optimal’ for a specific prior are indeed better denoised by this prior.
35#
發(fā)表于 2025-3-27 16:02:55 | 只看該作者
36#
發(fā)表于 2025-3-27 18:16:17 | 只看該作者
37#
發(fā)表于 2025-3-27 23:58:13 | 只看該作者
Deep Learning 3D Shape Surfaces Using Geometry Imagescut to convert the original 3D shape into a flat and regular geometry image. We propose a way to implicitly learn the topology and structure of 3D shapes using geometry images encoded with suitable features. We show the efficacy of our approach to learn 3D shape surfaces for classification and retrieval tasks on non-rigid and rigid shape datasets.
38#
發(fā)表于 2025-3-28 02:30:51 | 只看該作者
39#
發(fā)表于 2025-3-28 07:27:15 | 只看該作者
Learning Semantic Deformation Flows with 3D Convolutional Networksdetail information. Our experiments show that the CNN approach achieves comparable results with state of the art methods when applied to CAD models. When applied to single frame depth scans, and partial/noisy CAD models we achieve . less error compared to the state-of-the-art.
40#
發(fā)表于 2025-3-28 12:30:38 | 只看該作者
Conference proceedings 2016eo: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions..
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