Bibliographic record
Abstract
Point cloud semantic segmentation is a pivotal aspect of 3D scene comprehension and poses a significant challenge in point cloud processing. Despite the emergence of numerous deep learning methodologies for point cloud semantic segmentation in recent years, not many are directly applicable to large-scale outdoor point cloud segmentation, a critical component for understanding urban scenes. This chapter introduces an end-to-end network designed specifically for urban scene semantic segmentation, considering the unique challenges of large-scale outdoor scenes and the characteristics of 3D point clouds. The proposed deep learning network for point clouds incorporates three critical elements: (1) a robust and effective sampling approach for spatial downsampling of the point cloud; (2) a point-based feature abstraction module that efficiently encodes local features via spatial aggregation; (3) a tailored loss function that mitigates the imbalance across categories, thereby enhancing overall performance. To substantiate the effectiveness of the proposed network, two distinct datasets were employed for evaluation. The network demonstrated superior performance in the majority of the test data, achieving mean Intersection over Union (mIoU) scores of 70.8% and 73.9% on the Toronto-3D and Shanghai MLS datasets, respectively.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".