Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a real-time shape-assisted graph attention neural network to perform local pointcloud repairment. The orderless pointclouds require an effective shape encoder to distill local and global geometric feature descriptors. Previous work has attempted to convert pointcloud representation into a voxelized shape or perform grid-transformations. While these approaches can subsequently allow common convolution operations on the structured data, they either pose additional computational cost or disrupt the local geometric information. We present SAGA-Net, an efficient graph attention neural network framework with a prior shape inquiry protocol that effectively extracts local geometric information, locates the descriptor for the missing region and accurately reconstructs the local region in a real-time manner. Our framework has been validated on a benchmark dataset, ShapeNet. We demonstrate that our framework can repair each partial pointcloud with accuracy surpassing other frameworks in most object categories, and improve the computational efficiency by orders of magnitude in terms of time cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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