Adaptive Graph Convolution Algorithm Based on 3D Vision Selectivity and Its Application in Scene Segmentation
Bibliographic record
Abstract
At this stage, the 3D graph convolution algorithm has the following problems: (1) Neighbor space selection problem; (2) Feature extraction and fusion problem of different depth map convolution algorithms; (3) Multi-view parallel feature fusion problem.Based on this, "Multi-domain adaptive graph convolution algorithm based on visual computing theory and its scene segmentation application" is proposed.First, inspired by the 3D vision of primates, a 3D visual computing theory is proposed; and propose an adaptive graph convolution algorithm based on the 3D visual selectivity theory.It solves the problem of neighbor space selection for 3D graph convolution; secondly, inspired by the single-link serial processing mode of primate visual information, a single-link depth adaptive graph convolution algorithm based on 3D visual selectivity is constructed to learn and refuse the different depth visual features of the same sub-space of 3D point cloud; Finally, inspired by the multi-link parallel processing model of primate visual information, we improved the single-link algorithm and constructed a multi-link depth adaptive graph convolution algorithm based on 3D visual selectivity to learn and integrate global visual features of different link; and using the MLP algorithm with shared weights to achieve object segmentation.On ShapeNetPart and custom Mortise_and_Tenon_DB, Compare with PointNet, PointNet++, KPConv deform, 3D GCN and other algorithms.Verify the segmentation performance and geometric invariance of this algorithm.The experimental results: The segmentation performance of the algorithm of this article is good, and the segmentation success rate reaches 90.9%; the algorithm of this article has strong geometric invariance, Rotation and translation transformations are geometrically invariant, and Scaling transformation has finite geometric invariance in the interval [-0.15,0.15].
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".