Multi-modal Feature Fusion 3D Object Detection
Why this work is in the frame
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Bibliographic record
Abstract
For the existing 3D small object detection is prone to false detection and missed detection and other deficiencies. A 3D object detection method based on multi-modal feature fusion is proposed. Firstly, a feature extraction module is designed. The input image data is down-sampled through the image feature extraction network, and the input point cloud data is sampled and grouped through the point cloud feature extraction network to obtain the feature information at different scales. Secondly, a multi-modal feature fusion module is constructed to realize the point correspondence between point cloud features and image features by projection operation, and then the image features and point cloud features are splicing and fused to generate the final point cloud features to compensate the deficiency of single modal feature information. The experimental results show that compared with the existing algorithms, the algorithm in this paper improves the average detection accuracy of small object by 2.03%.
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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