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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".