Robust Point Cloud Normal Estimation via Multi-Level Critical Point Aggregation
Bibliographic record
Abstract
Abstract In this paper, we propose a multi-level critical point aggregation architecture for 3D point cloud normal estimation. It efficiently focuses on locally important points during feature extraction by employing our Local Feature Aggregation (LFA) and Global Feature Refinement (GFR) modules. These modules can accurately identify critical surface-fitting points across local and global levels. Specifically, the proposed LFA module aims to capture local geometric information from nearby points with strong correlation in low-level features, while our GFR module explores global geometric relationships in high-level features space, focusing on critical global points. Furthermore, utilizing a stacked LFA structure, we address indistinguishable features across multiple levels, enabling deep feature aggregation. By integrating multi-level features through the GFR module, our method effectively integrates robust local geometric information into comprehensive global features. This ensures stability and accuracy in subsequent surface fitting and normal estimation tasks, even in the presence of sharp features, high noise, or anisotropic structures. Experimental results demonstrate that our method is competitive and achieves stable performance on both synthetic and real-world datasets. Our implementation is available at https://github.com/CharlesLee96/NormalEstimation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".