Improved 3D Object Detector Under Snowfall Weather Condition Based on LiDAR Point Cloud
Bibliographic record
Abstract
LiDAR sensors are now used to supplement structure information and depth information for 3D object detection in automated driving. In adverse weathers, however, LiDAR tends to collect many noisy points in rainy or snowy days, which may disturb the results of object detection. In order to enhance the performance of the detector, we improve existing LiDAR-only 3D object detectors from two aspects under real snow weather condition. Firstly, double-attention block including point-wise attention and channel attention is applied to reweight the input feature of stacked pillars for crucial information extraction. Secondly, a lightweight and effective global context based pillar feature refinement extraction block is employed to capture long-range contextual information. It aims to filter local noisy information in the feature map, especially for the data collected in adverse weather conditions. Moreover, most of the previous works tend to focus on dataset under normal weather condition, so driving scenarios in adverse weather will bring challenges to the generalization of the model. Hence, to adapt our network to diverse domains better, we design a maximum mean discrepancy (MMD) block to get the distribution of domain feature representations as well as calculate the MMD loss in training process. Accordingly, the distribution discrepancy of two domains is narrowed. The performance evaluated on Canadian Adverse Driving Condition (CADC) Dataset collected in snowfall weather condition and KITTI dataset verifies the improvement of our approach. Code is available athttps://github.com/jiajia0408/i3detector_snowfall.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".