Efficient and Lightweight Semantic Segmentation Network for Land Cover Point Cloud With Local-Global Feature Fusion
Bibliographic record
Abstract
Utilizing deep learning techniques to extract high-precision features from point clouds is essential for accurately capturing land cover information, which is instrumental in urban planning and environmental conservation. Despite delivering high-accuracy outcomes in the semantic segmentation of extensive terrestrial point clouds, prevalent methodologies encounter considerable hurdles, particularly in training and inference duration, as well as the associated hardware expenses. To solve these issues, this paper introduces an efficient and lightweight deep learning network called Uniform Voxelization Geometric Enhancement and Local-Global Feature Fusion Network (VEF-Net). VEF-Net is designed to improve the training and inference efficiency of large-scale point cloud semantic segmentation while maintaining accuracy. Uniform voxel down-sampling is employed to discretize point clouds, resulting in a substantial enhancement in computational and memory performance. To counteract potential information loss due to voxel down-sampling, VEF-Net integrates a mechanism unit to enhance local geometric features, enriching point cloud data. Furthermore, it incorporates a Local-global feature fusion module, adeptly capturing the global contextual relationships within the point cloud. Experimental results show that VEF-Net achieved excellent performance on both the proprietary Bengbu dataset and the publicly available Toronto-3D dataset. VEF-Net maintained comparable segmentation accuracy to mainstream models while operating at a lower computational cost, and it demonstrated significant advantages in certain key tasks. On the Toronto3D dataset, VEF-Net achieved an IoU of 20.4% in the road marking category. Additionally, VEF-Net demonstrated lower model parameters and computational complexity (FLOPs), achieving training speeds 12 times faster and inference speeds 16.8 times faster than Point Transformer.
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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 | 0.000 | 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 | 0.002 | 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".