SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
This addresses the problem of efficient and accurate 6D pose estimation for robotics and AR applications, though it is incremental as it builds on the SSD paradigm.
The paper tackles 3D object detection and 6D pose estimation from RGB images by extending SSD to handle full 6D pose, achieving competitive or superior performance to RGB-D methods on multiple datasets and running at around 10Hz.
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 RGB-D data on multiple challenging datasets. Furthermore, our method produces these results at around 10Hz, which is many times faster than the related methods. For the sake of reproducibility, we make our trained networks and detection code publicly available.