A3
M-3LAB/awesome-3d-anomaly-detection
We have summarised all 3D anomaly detection methods and datasets (still updating). 多模态，点云和姿势无关异常检测的综述仓库（持续更新）
93 0 +0/wk
GitHub
3d anomaly-detection anomaly-segmentation awesome-lists computer-vision datasets graphics llms point-cloud reviews three-dimensional
Trend
3
Star & Fork Trend (14 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
M-3LAB/awesome-3d-anomaly-detection has +0 stars this period . 7-day velocity: 2.2%.
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Signal-backed technical analysis will be available soon.
| Metric | awesome-3d-anomaly-detection | awesome-human-activity-recognition | artifex | sophus-rs |
|---|---|---|---|---|
| Stars | 93 | 93 | 93 | 94 |
| Forks | 0 | 1 | 12 | 5 |
| Weekly Growth | +0 | +2 | +0 | +0 |
| Language | N/A | Python | Python | Rust |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | CC-BY-4.0 | NOASSERTION | Apache-2.0 |
Capability Radar vs awesome-human-activity-recognition
awesome-3d-anomaly-detection
awesome-human-activity-recognition
Maintenance Activity 100
Last code push 2 days ago.
Community Engagement 55
Fork-to-star ratio: 0.0%. Lower fork ratio may indicate passive usage.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.