Use Your Imagination: A Detector-Independent Approach for LiDAR Quality Booster
Bibliographic record
Abstract
Features from LiDAR and cameras are considered to be complementary. However, due to the sparsity of the LiDAR point clouds, a dense and accurate RGB/3D projective relationship is difficult to establish especially for distant scene points. Recent works try to solve this problem by designing a network that learns missing points or dense point density distributions to compensate for the sparsity of the LiDAR point cloud. In this work, we propose to use an imagine-and-locate process, called UYI. The objective of this module is to improve the point cloud quality and is independent of the detection network used for inference. We accomplish this task through a GAN based cross-modality module which uses an image as input to infer a dense LiDAR shape. Boosted by our UYI block, our experiments show a significant performance improvement in all tested baseline models. In fact, benefiting from the plug-and-play characteristics of our module, we were able to push the performance of existing state-of-the-art model to a new height. Code is available atwww.eecs.uottawa.ca/research/viva/projects/uyi.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".