A Study on the Impact of Domain Randomization for Monocular Deep 6DoF Pose Estimation
Bibliographic record
Abstract
In this work, we apply domain randomization to synthetic images and train deep 6DoF monocular RGB pose estimation models to work on a real object. We compare 19 models trained with different combinations of synthetic and real data (fully synthetic, fully real, initially synthetic and supplemented with real, and a real-synthetic randomized mix). By gradually decreasing the amount of real data used, we show it is possible for deep 6DoF detection to obtain superior results while using less real data (which is harder to obtain) and suggest the best approach to train a model with synthetic data. Our method is validated using a textureless 3D printed object, as the textureless category is a challenging, common open problem in itself. A real and a synthetic dataset generated for this work, totalling over 24,800 annotated frames, are also made public. We also show that synthetic, randomized data can help generalize a model by training it to handle challenges such as illumination changes and fast motion. Finally, we also evaluate how a model trained for one camera sensor works with a different one, and show that synthetic simulations of real cameras can help overcoming this challenge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".