Abstract

Part 1: Long and Short Papers (Continued); International audience; This paper investigates the effects of encumbrance (holding different types of objects while using mobile devices) to understand the interaction difficulties that it causes. An experiment was conducted where participants performed a target acquisition task on a touchscreen mobile phone while carrying different types of bags and boxes. Mobility was also evaluated since people carry items from one place to another. Motion capture hardware was used to track hand and arm postures to examine how holding the different types of objects caused excessive movement and instability therefore resulting in performance to decline. The results showed encumbrance and mobility caused target accuracy to decrease although input while holding the box under the non-dominant arm was more accurate and exerted quicker targeting times than holding no objects. Encumbrance affected the dominant hand more than the non-dominant hand as targeting error significantly increased and caused greater hand instability. The issues caused by encumbrance suggest the topic requires more attention from researchers and users would benefit greatly if better interaction techniques and applications are developed to counteract the problems. Document type: Part of book or chapter of book

Abstract

International audience; 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. 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. Document type: Part of book or chapter of book