RGGNet: Tolerance Aware LiDAR-Camera Online Calibration With Geometric Deep Learning and Generative Model
Why this work is in the frame
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Bibliographic record
Abstract
Accurate LiDAR-camera online calibration is critical for modern autonomous vehicles and robot platforms. Dominant methods heavily rely on hand-crafted features, which are not scalable in practice. With the increasing popularity of deep learning (DL), a few recent efforts have demonstrated the advantages of DL for feature extraction on this task. However, their reported performances are not sufficiently satisfying yet. We believe one improvement can be the problem formulation with proper consideration of the underneath geometry. Besides, existing online calibration methods focus on optimizing the calibration error while overlooking the tolerance within the error bounds. To address the research gap, a DL-based LiDAR-camera calibration method, named as the RGGNet, is proposed by considering the Riemannian geometry and utilizing a deep generative model to learn an implicit tolerance model. When evaluated on KITTI benchmark datasets, the proposed method demonstrates superior performances to the state-of-the-art DL-based methods. The code will be publicly available at https://github.com/KleinYuan/RGGNet.
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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 | 0.001 |
| 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 it