Real-Time Seamless Single Shot 6D Object Pose Prediction

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [Kehl et al. 2017] that only predicts an approximate 6D pose that must then be refined, ours is accurate enough not to require additional post-processing. As a result, it is much faster - 50 fps on a Titan X (Pascal) GPU - and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by [Redmon et al. 2016, Redmon and Farhadi 2017] that directly predicts the 2D image locations of the projected vertices of the object's 3D bounding box. The object's 6D pose is then estimated using a PnP algorithm. For single object and multiple object pose estimation on the LineMod and Occlusion datasets, our approach substantially outperforms other recent CNN-based approaches [Kehl et al. 2017, Rad and Lepetit 2017] when they are all used without post-processing. During post-processing, a pose refinement step can be used to boost the accuracy of these two methods, but at 10 fps or less, they are much slower than our method.

Presented at:
Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018
Jun 18 2018

 Record created 2018-03-14, last modified 2019-01-18

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