CL3D: Camera-LiDAR 3D Object Detection With Point Feature Enhancement and Point-Guided Fusion
Bibliographic record
Abstract
Camera-LiDAR 3D object detection has been extensively investigated due to its significance for many real-world applications. However, there are still of great challenges to address the intrinsic data difference and perform accurate feature fusion among two modalities. To these ends, we propose a two-stream architecture termed as CL3D, that integrates with point enhancement module, point-guided fusion module with IoU-aware head for cross-modal 3D object detection. Specifically, pseudo LiDAR is firstly generated from RGB image, and point enhancement module (PEM) is then designed to enhance the raw LiDAR with pseudo point. Moreover, point-guided fusion module (PFM) is developed to find image-point correspondence at different resolutions, and incorporate semantic with geometric features in a point-wise manner. We also investigate the inconsistency between localization confidence and classification score in 3D detection, and introduce IoU-aware prediction head (IoU Head) for accurate box regression. Comprehensive experiments are conducted on publicly available KITTI dataset, and CL3D reports the outstanding detection performance compared to both single- and multi-modal 3D detectors, demonstrating its effectiveness and competitiveness.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".