In this paper, we propose multi-layer structural wound synthesis on a 3D face. The fundamental knowledge of the facial skin is derived from the structure of tissue, being composed of epidermis, dermis and subcutis. The approach first defines the facial tissue depth map to measure details at various locations on the face. Each layer of skin in a wound image has been determined by hue-based segmentation. In addition, we have employed disparity parameters to realise 3D depth in order to make a wound model volumetric. Finally, we validate our methods using 3D wound simulation experiments.
Spatial representation capable of learning a myriad of environmental features is a significant challenge for natural spatial understanding of mobile AI agents.
Deep generative models have the potential of discovering rich representations of observed 3D scenes.
However, previous approaches have been mainly evaluated on simple environments, or focused only on high-resolution rendering of small-scale scenes, hampering generalization of the representations to various spatial variability.
To address this, we present PlaceNet, a neural representation that learns through random observations in a self-supervised manner, and represents observed scenes with triplet attention using visual, topographic, and semantic cues.
We evaluate the proposed method on a large-scale multimodal scene dataset consisting of 120 million indoor scenes, and show that PlaceNet successfully generalizes to various environments with lower training loss, higher image quality and structural similarity of predicted scenes, compared to a competitive baseline model.
Additionally, analyses of the representations demonstrate that PlaceNet activates more specialized and larger numbers of kernels in the spatial representation, capturing multimodal spatial properties in complex environments.
We introduce a novel perception-action-learning system for mobile social-service robots. The state-of-the-art deep learning techniques were incorporated into each module which significantly improves the performance in solving social service tasks. The system not only demonstrated fast and robust performance in a homelike environment but also achieved the highest score in the RoboCup2017@Home Social Standard Platform League (SSPL) held in Nagoya, Japan.
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