Towards Realistic LiDAR Intensity Simulation in Snowy Weather Using Physics-Informed Learning
Bibliographic record
Abstract
Simulating realistic LiDAR intensity is essential for autonomous driving, particularly under snow conditions, where current methods fail to capture complex LiDAR-to-atmosphere interactions. This paper introduces a CycleGAN framework guided by physics, which incorporates the principles of LiDAR intensity attenuation in snowy weather, significantly narrowing the simulation-to-reality gap. The model was evaluated using an open-source real snow dataset and an open-source simulated dataset, demonstrating its ability to replicate real-world intensity patterns with high accuracy, as indicated by metrics like Structural Similarity Index Measure (SSIM), Kullback-Leibler (KL) Divergence, etc. In the downstream semantic segmentation task, models trained on the enhanced data outperformed those trained on baseline datasets, underscoring the framework's effectiveness in improving LiDAR data realism and robustness in snow-weather autonomous driving scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".