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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >An Optimized BaySAC Algorithm for Efficient Fitting of Primitives in Point Clouds
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An Optimized BaySAC Algorithm for Efficient Fitting of Primitives in Point Clouds

机译：用于点云中基元有效拟合的优化BaySAC算法

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摘要

Fitting primitives is of great importance for remote sensing applications, such as 3-D modeling and as-built surveys. This letter presents a method for fitting primitives that fuses the Bayesian sample consensus (BaySAC) algorithm with a statistical testing of candidate model parameters for unorganized 3-D point clouds. Instead of randomly choosing initial data sets, as in the random sample consensus (RANSAC), we implement a conditional sampling method, which is the BaySAC, to always select the minimum number of data required with the highest inlier probabilities. As the primitive parameters calculated by the different inlier sets should be convergent, this letter presents a statistical testing algorithm for the histogram of the candidate model parameter to compute the prior probability of each data point. Moreover, the probability update is implemented using the simplified Bayes formula. The proposed approach is tested with the data sets of planes, tori, and curved surfaces. The results show that the proposed optimized BaySAC can achieve high computational efficiency (five times higher than the efficiency of the RANSAC for fitting a subset of 12 500 points) and high fitting accuracy (on average, 20% higher than the accuracy of the RANSAC). Moreover, the strategy of prior probability determination is proven to be model-free and, thus, highly applicable.
机译：拟合原语对于遥感应用（例如3-D建模和竣工测量）非常重要。这封信提出了一种适合基元的方法，该方法将贝叶斯样本共识（BaySAC）算法与针对无组织3-D点云的候选模型参数的统计测试融合在一起。我们没有像随机样本共识（RANSAC）中那样随机选择初始数据集，而是实现了条件抽样方法（即BaySAC），以始终选择具有最高内部概率的最少数据量。由于由不同的内部集计算得出的原始参数应该是收敛的，因此这封信提出了一种统计测试算法，用于候选模型参数的直方图，以计算每个数据点的先验概率。此外，使用简化的贝叶斯公式来实现概率更新。使用平面，圆托面和曲面的数据集对提出的方法进行了测试。结果表明，提出的优化BaySAC可以实现较高的计算效率（比RANSAC拟合12 500点子集的效率高5倍）和较高的拟合精度（平均比RANSAC的精度高20％）。 。此外，先验概率确定策略被证明是无模型的，因此具有很高的适用性。

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