  6D object pose tracking has been extensively studied in the robotics and
computer vision communities. The most promising solutions, leveraging on deep
neural networks and/or filtering and optimization, exhibit notable performance
on standard benchmarks. However, to our best knowledge, these have not been
tested thoroughly against fast object motions. Tracking performance in this
scenario degrades significantly, especially for methods that do not achieve
real-time performance and introduce non negligible delays. In this work, we
introduce ROFT, a Kalman filtering approach for 6D object pose and velocity
tracking from a stream of RGB-D images. By leveraging real-time optical flow,
ROFT synchronizes delayed outputs of low frame rate Convolutional Neural
Networks for instance segmentation and 6D object pose estimation with the RGB-D
input stream to achieve fast and precise 6D object pose and velocity tracking.
We test our method on a newly introduced photorealistic dataset, Fast-YCB,
which comprises fast moving objects from the YCB model set, and on the dataset
for object and hand pose estimation HO-3D. Results demonstrate that our
approach outperforms state-of-the-art methods for 6D object pose tracking,
while also providing 6D object velocity tracking. A video showing the
experiments is provided as supplementary material.
