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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

Shape Reconstruction Using Volume Sweeping and Learned Photoconsistency

Figure 7. Challenging scene captured with Kinovis. (left) one input image, (center) reconstructions obtained with our previous work based on classical 2D features, (right) proposed solution. Our results validate the key improvement of a CNN-learned disparity to MVS for performance capture scenarios. Results particularly improve in noisy, very low contrast and low textured regions such as the arm, the leg or even the black skirt folds.
IMG/vincent18.png

The rise of virtual and augmented reality fuels an increased need for contents suitable to these new technologies including 3D contents obtained from real scenes (see figure 7). We consider in this paper the problem of 3D shape reconstruction from multi-view RGB images. We investigate the ability of learning-based strategies to effectively benefit the reconstruction of arbitrary shapes with improved precision and robustness. We especially target real life performance capture, containing complex surface details that are difficult to recover with existing approaches. A key step in the multi-view reconstruction pipeline lies in the search for matching features between viewpoints in order to infer depth information. We propose to cast the matching on a 3D receptive field along viewing lines and to learn a multi-view photoconsistency measure for that purpose. The intuition is that deep networks have the ability to learn local photometric configurations in a broad way, even with respect to different orientations along various viewing lines of the same surface point. Our results demonstrate this ability, showing that a CNN, trained on a standard static dataset, can help recover surface details on dynamic scenes that are not perceived by traditional 2D feature based methods. Our evaluation also shows that our solution compares on par to state of the art reconstruction pipelines on standard evaluation datasets, while yielding significantly better results and generalization with realistic performance capture data.

This work has been published in the European Conference on Computer Vision 2018 [9] and Reconnaissance des Formes, Image, Apprentissage et Perception 2018 [8].