Multi-Granularity Feature Fusion For Point Cloud Semantic Segmentation Under Urban Scenes
Bibliographic record
Abstract
Point cloud semantic segmentation plays a key role in scene understanding and digital twin cities tasks. This article proposed a multi-granularity feature fusion network (MGF-Net) for point cloud semantic segmentation. The model first used a cluster relation aggregation module to extract fine-grained point features and a 3D convolution module to extract coarse-grained voxel features, followed by feature aggregation via a multi-granularity feature adaptive fusion module. Finally, to further improve the model performance, MGF-Net used a global feature attention module to capture long-distance context information. The performance of MGF-Net was evaluated on three point cloud datasets of urban scenes, i.e., Toronto3D, WHU-MLS, and SensatUrban. The quantitative results showed that MGF-Net achieved 80.16%, 51.27%, and 54.20% of mIoU on these datasets, respectively. Moreover, the comparative results showed that the proposed MGF-Net outperformed the baseline for complex urban scenes, and obtained better point cloud semantic segmentation results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".