GAF-Net: Geometric Contextual Feature Aggregation and Adaptive Fusion for Large-Scale Point Cloud Semantic Segmentation
Bibliographic record
Abstract
Large-scale point cloud semantic segmentation is a challenging task due to the complexity and diversity of real-world 3D scenes. Most existing methods primarily rely on spatial coordinates to learn geometric representations without fully exploring local structural relationships. Additionally, the semantic gap between the encoder and decoder in segmentation networks is an important factor that constrains model performance. To address these challenges, we propose a novel network architecture called GAF-Net, which comprises a Geometric Contextual Feature Aggregation (GCFA) module and a Multi-scale Feature Adaptive Fusion (MFAF) module. The GCFA module consists of three primary blocks: (1) a Geometric Edge Representation block, designed to leverage spatial relative position and orientation information between the center point and its neighbors to capture detailed local geometric structural relations; (2) a Point Geometry Prior block, aimed at extracting explicit geometric priors for each point from raw point clouds. This block is lightweight and parameter-free; (3) a Geometry-Aware Attentive Pooling block, which combines semantic features with learned geometric representations, enabling the learning and aggregation of informative local contextual features. Our proposed MFAF module integrates multi-scale features by introducing an adaptive fusion approach. It effectively bridges the semantic gap between the encoder and decoder and mitigates the information loss caused by random sampling. Extensive experimental results on three large-scale benchmark datasets including S3DIS, Toronto3D, and SemanticKITTI demonstrate the superior performance of our proposed GAF-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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".