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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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Section: New Results

FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis

Figure 8. Two examples of texture transfer from a reference shape in neutral pose (left) using shape correspondences predicted by FeaStNet (multi-scale architecture, without refinement).
IMG/nitika18.png

Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. CNNs do not easily extend, however, to data that are not represented by regular grids, such as 3D shape meshes or other graph-structured data, to which traditional local convolution operators do not directly apply. To address this problem, we propose a novel graph-convolution operator to establish correspondences between filter weights and graph neighborhoods with arbitrary connectivity. The key novelty of our approach is that these correspondences are dynamically computed from features learned by the network, rather than relying on predefined static coordinates over the graph as in previous work. We obtain excellent experimental results that significantly improve over previous state-of-the-art shape correspondence results (see figure 8). This shows that our approach can learn effective shape representations from raw input coordinates, without relying on shape descriptors.

This work has been published in the IEEE Conference on Computer Vision and Pattern Recognition 2018 [11].