Estimation of 2D Bounding Box Orientation with Convex-Hull Points - A Quantitative Evaluation on Accuracy and Efficiency.
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Bibliographic record
Abstract
Estimating the bounding box from an object point cloud is an essential task in autonomous driving with LiDAR/laser sensors. We present an efficient bounding box estimation method that can be applied on 2D bird's-eye view (BEV) LiDAR points to generate the bounding box geometry including length, width and orientation. Given a set of 2D points, the method utilizes their convex-hull points to calculate a small set of candidate directions of the box yaw orientation, and therefore reduces the searching space – usually a fine partition of an angle range (e.g. [0, $\pi/2$ )) as in the previous solutions – to find the optinal angle. To further improve the efficiency, we investigate the techniques of controlling the number of convex-hull points, by both applying approximate collinearity condition and downsampling the raw point cloud to a smaller size. We provide comprehensive analysis on both accuracy and efficiency of the proposed method on the KITTI 3D object dataset. The results show that without obviously sacrificing the accuracy, the method, especially when using approximate convex-hull points, can significantly improve the time of estimating the bounding box orientation by almost one order of magnitude.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it