NBV-HRR: Next Best View Planning Network for Highly Reflective Region Restoration in Robotic 3-D Scanning
Bibliographic record
Abstract
Three-dimensional scanning of highly reflective surfaces is a challenge faced in industrial metrology. The high dynamic range (HDR) technique offers a solution by fusing images taken at various exposures. However, its effectiveness is limited in regions where reflectivity exceeds the camera's dynamic range. To address this issue, this article introduces a next best view (NBV) planning network for the restoration of highly reflective regions, named NBV-HRR. Unlike traditional NBV networks focused on complete shape reconstruction utilizing global features, NBV-HRR targets the restoration of localized regions lost due to reflection, utilizing both global and local features. To accurately evaluate each view, a cross-attention-based module is devised to fuse features of varying dimensions. For network training, a dataset containing 70 400 training samples was collected from over 1000 highly reflective industrial parts in both simulation and real scenes. Experimental results demonstrated that the proposed NBV-HRR network achieved an average restoration rate of 99.3% within three NBV predictions, significantly outperforming the HDR technique (83.7%).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".