An Improved Approach to 6D Object Pose Tracking in Fast Motion Scenarios
Bibliographic record
Abstract
Tracking 6D poses of objects in video sequences is important for many applications such as robot manipulation and augmented reality. End-to-end deep learning based 6D pose tracking methods have achieved notable performance both in terms of accuracy and speed on standard benchmarks characterized by slowly varying poses. However, these methods fail to address a key challenge for using 6D pose trackers in fast motion scenarios. The performance of temporal trackers degrades significantly in fast motion scenarios and tracking failures occur frequently. In this work, we propose a framework to make end-to-end 6D pose trackers work better for fast motion scenarios. We integrate the “Relative Pose Estimation Network” from an end-to-end 6D pose tracker into an EKF framework. The EKF adopts a constant velocity motion model and its measurement is computed from the output of the “Relative Pose Estimation Network”. The proposed method is evaluated on challenging hand-object interaction sequences from the Laval dataset and compared against the original end-to-end pose tracker, referred to as the baseline. Experiments show that integration with EKF significantly improves the tracking performance, achieving a pose detection rate of 85.23% compared to 61.32% achieved by the baseline. The proposed framework exceeds the real-time performance requirement of 30 fps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".