A LiDAR-Based Kolmogorov-Arnold Point Transformer Algorithm for Semantic Segmentation of Urban 3D Point Clouds
Bibliographic record
Abstract
Accurate semantic segmentation of 3D point clouds is essential for autonomous driving, HD map updating, and road asset management. Although Point Transformer models local-global geometry with self-attention, its MLP-based feature transformations may limit representational flexibility. We propose KAN-Point Transformer by integrating a Jacobi-polynomial Kolmogorov-Arnold Network (KAN) into the Transformer Block to replace the key MLP-based transformations. On ShapeNet Part, our method improves Cat.mIoU from 76.0% to 78.1% and Ins.mIoU from$\mathbf{8 2. 7 \%}$to$\mathbf{8 4. 2 \%}$, with the best setting at$\boldsymbol{\alpha}=\boldsymbol{\beta}=\mathbf{1. 0}$and degree$\mathrm{D}=4$. On Toronto-3D with identical xyz+RGB inputs, it raises mIoU from 65.63% to 72.67%, and boosts road marking IoU from 9.69% to 59.24%. These results show that KAN-Point Transformer better exploits RGB cues and improves multimodal feature utilization in real-world segmentation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".