De-noising of Lidar Point Clouds Corrupted by Snowfall
Bibliographic record
Abstract
A common problem in autonomous driving is designing a system that can operate in adverse weather conditions. Falling rain and snow tends to corrupt sensor measurements, particularly for lidar sensors. Surprisingly, very little research has been published on methods to de-noise point clouds which are collected by lidar in rainy or snowy weather conditions. In this paper, we present a method for removing snow noise by processing point clouds using a 3D outlier detection algorithm. Our method, the dynamic radius outlier removal filter, accounts for the variation in point cloud density with increasing distance from the sensor, with the goal of removing the noise caused by snow while retaining detail in environmental features (which is necessary for autonomous localization and navigation). The proposed method outperforms other noise-removal methods, including methods which operate on depth image representations of the lidar scans. We show on point clouds obtained while driving in falling snow that we can simultaneously obtain > 90% precision and recall, indicating that the proposed method is effective at removing snow, without removing environmental features.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".