LiCaS3: A Simple LiDAR–Camera Self-Supervised Synchronization Method
Bibliographic record
Abstract
Recent advances in robotics and deep learning demonstrate promising 3-D perception performances via fusing the light detection and ranging (LiDAR) sensor and camera data, where both spatial calibration and temporal synchronization are generally required. While the LiDAR–camera calibration problem has been actively studied during the past few years, LiDAR–camera synchronization has been less studied and mainly addressed by employing a conventional pipeline consisting of clock synchronization and temporal synchronization. The conventional pipeline has certain potential limitations, which have not been sufficiently addressed and could be a bottleneck for the potential wide adoption of low-cost LiDAR–camera platforms. Different from the conventional pipeline, in this article, we propose the LiCaS3, the first deep-learning-based framework, for the LiDAR–camera synchronization task via self-supervised learning. The proposed LiCaS3 does not require hardware synchronization or extra annotations and can be deployed both online and offline. Evaluated on both the KITTI and Newer College datasets, the proposed method shows promising performances. The code will be publicly available athttps://github.com/KleinYuan/LiCaS3.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".