A Local-Global Feature Fusing Method for Point Clouds Semantic Segmentation
Bibliographic record
Abstract
In recent years, the abundance of information in 3D data has made the semantic segmentation of 3D point clouds a topic of great interest. However, current methods often rely solely on the original three-dimensional coordinates of the point cloud as input geometric features, leading to poor generalization performance. Additionally, occlusion of the point cloud data can negatively impact segmentation accuracy when only local information is considered. To address these issues, this paper proposes a network named LGFF-Net. To fully utilize the original information of point clouds, we designed a Local Feature Aggregation (LFA) module that treats geometric and semantic information equally and preserves the original properties while cross-augmenting them. On the other hand, we proposed a simple and effective Global Feature Extraction (GFE) module to extract global features. Finally, we hierarchically fuse local and global features using a U-shaped segmentation structure. Compared to state-of-the-art networks, our method achieves competitive results on several benchmark datasets, including Semantic Topographic Point Labeling-Synthetic 3D, Toronto_3D, Stanford Large 3-D Indoor Space, and ScanNet. We also conduct multiple ablation experiments to validate the efficacy of LGFF-Net.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".