Sequential Fusion via Bounding Box and Motion PointPainting for 3D Objection Detection
Bibliographic record
Abstract
Due to the complementary characteristics of camera and LiDAR data, recent research efforts have been focused on designing 3D object detectors capable of fusing images and point clouds. However, LiDAR-based detectors currently achieve better performance on KITTI and Waymo benchmark datasets [1], [2] when compared to fusion methods. This result is counter-intuitive, as fusing information from the two modalities should result in performance that at least matches the performance of LiDAR-only methods. Pointpainting [3] attempts to address this gap by sequential fusion, which solves the issue of misalignment between image view and LiDAR BEV. In this paper, we propose class-aware and class-agnostic point painting methods which employ predicted bounding boxes from image-based 2D object detectors to extract coarse image semantics instead of full scene semantic segmentation used in [3]. In addition, a motion point painting method is proposed to fuse motion cues as a way to focus attention on dynamic objects when they can be reliably distinguished from the scene, as is the case when the sensors are static. Our experiments on KITTI [1] show a 3% mAP improvement on car class for bounding box methods compared to PointPainting [3]. In addition, motion painting shows an improvement of 1.45% mAP for car class and 2.99% for pedestrian class on our proprietary traffic dataset. Finally, we conduct a range-binned evaluation on KITTI dataset using two different LiDAR stream and show that relative gain of sequential fusion methods is dependent on the selected LiDAR stream.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".