.png){kind=icon}
.png){kind=icon}
Return
Efficient rock joint detection from large-scale 3D point clouds using vectorization and parallel computing approaches
.png){kind=icon}
OA
AI
DOI：10.1016/j.gsf.2025.102085.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• A method was proposed to rapidly identify rock joints from big data (>107 points). • Two parameters (point normal and point curvature) were determined as inputs to ANNs. • The algorithm structure was optimized via vectorization to boost efficiency. • Parallel computing was performed to achieve the high performance of the algorithms. • Time for rock joint detection was reduced by 3–4 times via optimization algorithms.
Keywords:
Rock joints
Point clouds
Artificial neural network
High-performance computing
Parallel computing
Vectorization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
2.0K
Citations:
1.2W