TFNet: point cloud Semantic Segmentation Network based on Triple feature extraction
Bibliographic record
Abstract
Semantic segmentation of point clouds plays a crucial role in computer vision, with diverse applications in urban modelling, autonomous driving, and virtual reality. Despite its significance, many existing methods face challenges when dealing with large-scale datasets, such as (1) unclear or incomplete boundary segmentation and (2) poor performance on sparse objects. These limitations stem from inadequate local context extraction and insufficient handling of density variations, which hinder the accuracy and robustness of segmentation. To address these challenges, we propose TFNet, an end-to-end deep neural network specifically designed to enhance local geometric feature extraction and improve performance on density variations. TFNet introduces three key components: (1) Rotation-Invariant and Geometric Feature Extractor (RIGFE), which independently captures rotation-invariant and geometric features; (2) Annularly Convolutional Attention Pooling (ACAP), which leverages annular convolution for effective relational feature extraction in both feature and geometric spaces; and (3) Subgraph Vector of Locally Aggregated Descriptors (SGVLAD), which learns position- and scale-invariant point set features. Experimental evaluations on benchmark datasets, including S3DIS, Toronto-3D, and Nanning Power Grid, demonstrate that TFNet outperforms existing methods by effectively addressing these challenges. The results highlight its ability to deliver superior segmentation accuracy and robustness in diverse scenarios.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".