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Multi-model fitting based on minimum spanning tree
This paper presents a novel approach to the computation of primitive geometrical structures, where no prior knowledge about the visual scene is available and a high level of noise is expected. We based our work on the grouping principles of…
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Lighting-Up Geometry: Accurate 3D Modelling of Museum Artifacts with a Torch and a Camera
This paper addresses the problem of obtaining complete, very detailed reconstructions of shiny objects such as glazed ceramics. We present an algorithm which uses silhouettes of the object, as well as images obtained under changing illumination conditions. In contrast with…
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Optimization of Facade Segmentation Based on Layout Priors
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Multi-camera Torso Pose Estimation using Graph Neural Networks
Estimating the location and orientation of humans is an essential skill for service and assistive robots. To achieve a reliable estimation in a wide area such as an apartment, multiple RGBD cameras are frequently used. Firstly, these setups are relatively…
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Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines
The novelty of this study consists in a multi-modality approach to scenenclassification, where image and audio complement each other in a process ofndeep late fusion. The approach is demonstrated on a difficult classificationnproblem, consisting of two synchronised and balanced datasets…
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Traffic3D: A New Traffic Simulation Paradigm
The field of Deep Reinforcement Learning has evolved significantly over the last few years. However, an important and not yet fully-attained goal is to produce intelligent agents which can be successfully taken out of the laboratory and employed in the…
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Dress Like a Star: Retrieving Fashion Products from Videos
This work proposes a system for retrieving clothing and fashion products from video content. Although films and television are the perfect showcase for fashion brands to promote their products, spectators are not always aware of where to buy the latest…
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rcGAN: Learning a Generative Model for Arbitrary Size Image Generation