LCE-NET: Contour Extraction for Large-Scale 3-D Point Clouds
Bibliographic record
Abstract
The contours, one of the most significant human perceptual features, have a significant impact on point cloud processing. In urban scenes, contour extraction is quite challenging due to the enormous number of unstructured and irregular points (typically greater than 107points). In this paper, we propose a Large-scale 3D point cloud Contour Extraction Network (LCE-NET) to generate contours consistent with human perception of outdoor scenes. To our knowledge, it is the first time that an end-to-end learning-based framework has been proposed for contour extraction on point cloud at this scale. The proposed LCE-Net is essentially a two-phase system. In the first phase, potential vertexes are detected from the input point cloud by the vertex detection module, then in the second phase, a designed overcomplete line proposal set is generated, and invalid line segments are further suppressed by the line proposal discrimination module. The two phases are jointly trained by a uniform loss function to promote the information interchange, thus leading to extraction results with satisfied accurate and false alarm ratings. Since there is hardly any available dataset with labeled contours for the large-scale outdoor scene, we open sourced SemanticLine, the first dataset for large-scale point clouds with labeled contour information, based on re-annotation of previous mapping level point cloud dataset semantic3D. Experimental results demonstrate that LCE-NET can effectively extract parametric contour lines from large-scale point clouds of urban scenes. Additionally, it outperforms the state-of-the-art approaches. The code will be open source on GitHub soon.
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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 | 0.001 | 0.002 |
| 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 | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".