针对精细化实景三维建筑物建模，提出一种基于改进遗传算法（IGA）的建筑物LiDAR点云与正射影像融合提取方法：计算并提取基于点云和影像的特征，实现点云特征空间的扩张；再改进遗传算法选择点云特征，构建并优化特征空间；最后使用SVM分类器实现建筑物点云的精准提取。在ISPRS公开数据集Vaihingen测试数据的试验表明本文方法具有较高的建筑物提取精度；在实际生产数据的实验表明建筑物提取精度较高且稳定，证明了本文方法的先进性和普适性。

This paper proposes a method for extracting building LiDAR point cloud and orthophoto fusion based on improved genetic algorithm (IGA) for fine-grained 3D building modeling: The features based on point cloud and image are calculated and extracted to expand the feature space of point cloud; then, by using the improved genetic algorithm, the point cloud features are selected, construct and optimize feature space; finally, SVM classifier is used to achieve accurate extraction of building point cloud. The experimental results on ISPRS open data set Vaihingen test data show that the method proposed in this paper has high accuracy in building extraction. The experimental results on actual production data show that the building extraction accuracy is high and stable, which proves the advancement and universality of this method.