ELMF-Net: Semantic Segmentation of Large-Scale Point Clouds via Efficient Local Feature Learner and Multiscale Fusion
Bibliographic record
Abstract
The semantic segmentation of 3-D point clouds can precisely describe 3-D environmental information, serving as an important research direction for environmental perception in unmanned systems. However, existing methods face drawbacks owing to the limitations in local semantic feature representation and cross-scale information fusion capabilities. To address these issues, we propose ELMF-Net, an efficient and accurate semantic segmentation model for large-scale 3-D point clouds. First, we introduce a local feature learning method that does not rely on strict geometric relationships and establish a local feature learner (w-LFL) model to capture and aggregate locally semantic discriminative features from point clouds. Subsequently, a novel multiscale feature fusion (MSFF) module was designed to collaborate with the decoder to deeply integrate shallow encoding layer features at different resolutions and high-level semantic features from deep encoding layers, providing an efficient representation of objects with varying scales. Finally, we validate the performance of ELMF-Net on three large-scale datasets, Stanford large-scale 3D indoor spaces dataset (S3DIS), Toronto3D, and Semantic Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI), demonstrating the excellent performance of the ELMF-Net network in large-scale, multitarget scene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".