Quick and high-precision semantic segmentation from Mobile Laser Scanning (MLS) point clouds faces huge challenges such as large amounts of data, occlusion in complex scenes, and the high annotation cost associated with 3D point clouds. To tackle these challenges, this paper proposes a novel efficient and high-precision semantic segmentation method Mapping Considering Semantic Segmentation (MCSS) for MLS point clouds by leveraging the 2D-3D mapping relationship, which is not only without the need for labeling 3D samples but also complements missing information using multimodal data. According to the results of semantic segmentation on panoramic images by a neural network, a multi-frame mapping strategy and a local spatial similarity optimization method are proposed to project the panoramic image semantic predictions onto point clouds, thereby establishing coarse semantic information in the 3D domain. Then, a hierarchical geometric constraint model (HGCM) is designed to refine high-precision point cloud semantic segmentation. Comprehensive experimental evaluations demonstrate the effect and efficiency of our method in segmenting challenging large-scale MLS two datasets, achieving improvement by 16.8 % and 16.3 % compared with SPT. Furthermore, the proposed method takes an average of 8 s to process 1 million points and does not require annotation and training, surpassing previous methods in terms of efficiency.

中文翻译：

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来自 MLS 点云的高效多模态高精度语义分割，无需 3D 注释


移动激光扫描 （MLS） 点云的快速、高精度语义分割面临巨大的挑战，例如数据量大、复杂场景下的遮挡以及与 3D 点云相关的高标注成本。为了应对这些挑战，本文利用 2D-3D 映射关系，提出了一种新的高效、高精度的语义分割方法——考虑语义分割的 MLS 点云映射 （MCSS），该方法不仅不需要标记 3D 样本，而且使用多模态数据补充了缺失的信息。根据神经网络对全景图像进行语义分割的结果，提出了一种多帧映射策略和局部空间相似性优化方法，将全景图像语义预测投射到点云上，从而在三维领域建立粗略语义信息。然后，设计分层几何约束模型 （HGCM） 来细化高精度点云语义分割。全面的实验评估证明了我们的方法在分割具有挑战性的大规模 MLS 两个数据集方面的效果和效率，与 SPT 相比分别提高了 16.8% 和 16.3%。此外，所提出的方法平均需要 8 s 来处理 100 万个点，并且不需要注释和训练，在效率方面超过了以前的方法。