GMFE-Net: point cloud semantic segmentation with general multi-feature fusion and extraction
Bibliographic record
Abstract
Semantic segmentation serves an essential role in the comprehension of 3D scenes. Existing methods mostly adopt a U-Net architecture model, where point cloud feature maps are extracted during the encoder stage and restored using transposed convolution in the decoder stage. However, transposed convolution does not fully reconstruct the original feature maps, leading to information loss. To mitigate this issue and enhance feature learning, we propose the GMFE-Unit, which integrates a local multi-feature fusion and extraction block, an attention-pooling block, and a global multi-feature fusion and extraction block. Based on this, we develop GMFE-Net, which applies GMFE-Unit in both encoder and decoder stages, replacing the traditional transposed convolution in the decoder to improve feature fusion and extraction. We evaluated GMFE-Net and achieve high semantic segmentation accuracy across three datasets from different sources, SensatUrban, DALES, and EVO. Furthermore, we conduct the first comprehensive analysis of how various point cloud attribute features (e.g., RGB, intensity, echo deviation, etc.) impact semantic segmentation. We further reveal the underlying reasons for their influence on segmentation performance. The code of our key contributions is publicly accessible at: https://github.com/LiTao-0797/GMFE-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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".