We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model.
In this work, we develop a multi-modality tracker that fuses information from visual appearance and geometry to estimate object poses.
Ranked #1 on 6D Pose Estimation on YCB-Video
Our approach focuses on methods that employ Newton-like optimization techniques, which are widely used in object tracking.
Ranked #1 on 3D Object Tracking on RTB
Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision.
Ranked #2 on 6D Pose Estimation on OPT
Finally, we use a pre-rendered sparse viewpoint model to create a joint posterior probability for the object pose.