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We present a novel method for detecting 3D model instances and estimatingtheir 6D poses from RGB data in a single shot. To this end, we extend thepopular SSD paradigm to cover the full 6D pose space and train on syntheticmodel data only. Our approach competes or surpasses current state-of-the-artmethods that leverage RGB-D data on multiple challenging datasets. Furthermore,our method produces these results at around 10Hz, which is many times fasterthan the related methods. For the sake of reproducibility, we make our trainednetworks and detection code publicly available.

SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again