Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation
This addresses the problem of accurate and stable object pose tracking for applications like augmented reality, though it is incremental as it builds on existing keypoint-based methods.
The paper tackles category-level 6-DoF object pose estimation from monocular RGB video by proposing a single-stage algorithm that detects and tracks instances, integrating uncertainties from previous frames to improve accuracy and stability. It outperforms existing methods on the Objectron benchmark.
We propose a single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category. Our method takes as input the previous and current frame from a monocular RGB video, as well as predictions from the previous frame, to predict the bounding cuboid and 6-DoF pose (up to scale). Internally, a deep network predicts distributions over object keypoints (vertices of the bounding cuboid) in image coordinates, after which a novel probabilistic filtering process integrates across estimates before computing the final pose using PnP. Our framework allows the system to take previous uncertainties into consideration when predicting the current frame, resulting in predictions that are more accurate and stable than single frame methods. Extensive experiments show that our method outperforms existing approaches on the challenging Objectron benchmark of annotated object videos. We also demonstrate the usability of our work in an augmented reality setting.