Paintedpillars: Efficient 3D Object Detection Using Only StatisticalProcessing To Compute Pillar Features with Explicit Class Probability Distributions
Bibliographic record
Abstract
In autonomous driving technology, high-speed and highly accurate three-dimensional object detection is necessary to accurately grasp the surrounding environment, and it is important to integrate information from multiple sensors.Conventional methods have performed 3D object detection by combining distance information from LiDAR (Light Detection And Ranging) and texture information from camera images, but there were issues with the amount of calculation and accuracy.Therefore, we propose a new method called PaintedPillars that explicitly assigns a class probability distribution to each pillar.Here, a pillar is a column-shaped space extending vertically into each division of a horizontal plane divided into a grid in a three-dimensional space.The proposed method, through statistical processing for each pillar, calculates class probability distributions from the semantic segmentation results of camera images, and obtains the spatial distribution of points from LiDAR data.In other words, the feature values for each pillar required for threedimensional object detection, that is, object type discrimination and bounding box estimation, are obtained using only statistical processing.The amount of calculation is proportional to the product of the number of points and the number of input channels, and does not depend on the number of output channels, so it is less than the conventional method using neural networks.Furthermore, the problem of reduced accuracy due to padding does not occur.Compared to the conventional PointPainting, experiments showed that the detection accuracy of the proposed method was 1 % higher, and the execution time and GPU memory usage were reduced to 33 % and 60 %, respectively, demonstrating its effectiveness.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".