摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据新闻报道NewsRx编辑对中国人民共和国西安的报道称，“为了改善”本研究提出了用pho测量联合糙率系数(JRC)的精度基于地面采样距离(GSD)、点密度和均方根误差的优化模型(RMSE)个检查站。首先，一种自动生成设备空间位置的算法在此基础上,运用运动结构原理和多目标原理,提出了收敛策略查看St EREO(SfM-MVS)和拍摄参数选择算法(SPSA)。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting out of Xi’an, People’s Republ ic of China, by NewsRx editors, research stated, “To improvethe accuracy of pho togrammetric joint roughness coefficient (JRC) estimation, this study proposes t wooptimization models based on ground sample distance (GSD), point density, and the root mean square error(RMSE) of checkpoints. First, an algorithm that auto matically generates spatial positions for equipmentbased on the convergence str ategy was developed, using principles of Structure from Motion and Multi-View St ereo (SfM-MVS) and the shooting parameter selection algorithm (SPSA).”
NETL
NSTL