Multi-KPConv: deep learning-based LiDAR point cloud ground point extraction for complex terrains on the Loess Plateau
Bibliographic record
Abstract
Accurate extraction of ground points from LiDAR point clouds provides important data for understanding terrain changes and supports decision-making in ecological disaster prevention. Recently, deep learning models have been used to process point clouds directly, with a focus on semantic segmentation of urban scenes using RGB features. However, in complex terrains, using point cloud images to generate RGB features often introduces noise, making high-precision ground point extraction a difficult task. This paper presents a new point-based semantic segmentation network, Multi-KPConv, to overcome these challenges. Unlike methods that rely on color point clouds, Multi-KPConv uses shallow features based on domain knowledge as input to the network. The network employs a multi-dimensional kernel point convolutional architecture to extract high-level semantic features, allowing for better data interpretation. Additionally, the SimAM 3D attention mechanism is integrated to adaptively refine feature contributions and highlight important point features. We evaluate Multi-KPConv on datasets from the Dafosi mining area in China’s Loess Plateau and the STPLS3D dataset. Experimental results show that Multi-KPConv outperforms current state-of-the-art models in terms of generalization and robustness, effectively extracting ground points in complex terrain, such as the gully areas of the Loess Plateau.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".