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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".