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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...
Intense research activity on 3D data analysis tasks, such as object recognition and shape retrieval, has recently fostered the proposal of many techniques to perform detection of repeatable and distinctive key points in 3D surfaces. This high number of proposals has not been accompanied yet by a comprehensive comparative evaluation of the methods. Motivated by this, our work proposes a performance...
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...
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