Feature Graph Convolution Network With Attentive Fusion for Large-Scale Point Clouds Semantic Segmentation
Bibliographic record
Abstract
Unstructured nature of 3D point clouds in large scenes is a challenging problem to effectively learn local geometric structures for point cloud semantic segmentation. To address this issue, we proposed a Feature Graph Convolution Network with Attentive Fusion (FGC-AFNet) in this letter. Our method takes large point clouds as input and uses the Feature Graph Convoluton (FGC) module to construct a graph of the central point with its neighboring points to extract local features. Then, we reduced the number of points using Random Sampling (RS) to expand the receptive field gradually to obtain multi-level features. The network also employs a dual Attention Fusion (AF) mechanism for efficient feature aggregation. One is at different levels for semantic feature fusion, another is for narrowing the semantic feature gap between the encoder and decoder. Compared to state-of-the-art methods on the S3DIS and Toronto3D datasets, our method obtained competitive results, with an overall accuracy of 88.6% and 96.58%, and a mean intersection over union of 71.2% and 81.92% on S3DIS and Toronto3D, respectively.
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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 | 0.002 | 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".