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We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover the full 6D pose space and train on synthetic model data only. Our approach competes or surpasses current state-of-the-art methods that leverage RGBD data on multiple challenging datasets. Furthermore, our method produces...
This work aims at automatic detection of man-made pole-like structures in scans of urban environments acquired by a 3D sensor mounted on top a moving vehicle. Pole-like structures, such as e.g. roadsigns and streetlights, are widespread in these environments, and their reliable detection is relevant to applications dealing with autonomous navigation, facility damage detection, city planning and maintenance...
We propose a method to perform automatic segmentation of 3D scenes based on a standard classifier, whose learning model is continuously improved by means of new samples, and a grouping stage, that enforces local consistency among classified labels. The new samples are automatically delivered to the system by a feedback loop based on a feature selection approach that exploits the outcome of the grouping...
Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization to the case of multiple cues associated with...
Object segmentation in 3D data such as 3D meshes and range maps is an emerging topic attracting increasing research interest. This work proposes a novel method to perform segmentation relying on the use of 3D features. The deployment of a specific grouping algorithm based on a Markov Random Field model successively to classification allows at the same time yielding automatic segmentation of 3D data...
In this work we propose a novel Hough voting approach for the detection of free-form shapes in a 3D space, to be used for object recognition tasks in 3D scenes with a significant degree of occlusion and clutter. The proposed method relies on matching 3D features to accumulate evidence of the presence of the objects being sought in a 3D Hough space. We validate our proposal by presenting a quantitative...
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