Time-Encoded Iterative Interactive Segmentation for Point Cloud
Bibliographic record
Abstract
Point cloud interactive segmentation can simplify the process of manual labeling; however, existing methods are far from satisfactory and face several challenges to conquer. We propose one novel deep learning-based iterative interactive segmentation network for point cloud, named Time-encoded Iterative Interactive Segmentation Network (TiisNet). This method allows users to segment the objects of interest by interactively clicking on them. TiisNet comprises two key parts: a point cloud and clicks encoder, and a general training strategy. The point cloud and clicks encoder extracts spatial and temporal features from the point cloud and user clicks. The general training strategy enhances training efficiency and prevents network degradation. Ablation experiments verify the effectiveness of the training strategy. The comparative experiments also demonstrate that TiisNet achieves superior segmentation performance compared to existing method. The code is at TiisNet GitHub
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".