Seeing Glass: Joint Point Cloud and Depth Completion for Transparent\n Objects
Bibliographic record
Abstract
The basis of many object manipulation algorithms is RGB-D input. Yet,\ncommodity RGB-D sensors can only provide distorted depth maps for a wide range\nof transparent objects due light refraction and absorption. To tackle the\nperception challenges posed by transparent objects, we propose TranspareNet, a\njoint point cloud and depth completion method, with the ability to complete the\ndepth of transparent objects in cluttered and complex scenes, even with\npartially filled fluid contents within the vessels. To address the shortcomings\nof existing transparent object data collection schemes in literature, we also\npropose an automated dataset creation workflow that consists of\nrobot-controlled image collection and vision-based automatic annotation.\nThrough this automated workflow, we created Toronto Transparent Objects Depth\nDataset (TODD), which consists of nearly 15000 RGB-D images. Our experimental\nevaluation demonstrates that TranspareNet outperforms existing state-of-the-art\ndepth completion methods on multiple datasets, including ClearGrasp, and that\nit also handles cluttered scenes when trained on TODD. Code and dataset will be\nreleased at https://www.pair.toronto.edu/TranspareNet/\n
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".