Deep learning for large-scale point cloud segmentation in tunnels considering causal inference

Zhang, Zhaoxiang; Ji, Ankang; Zhang, Limao; Xu, Yansheng; Zhou, Qing

doi:10.1016/j.autcon.2023.104915

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…However, this study specifically focused on circular tunnels, with a greater emphasis on segmenting the tunnel segments on the walls during the segmentation process. Zhang proposed an integrated segmentation model capable of effectively delineating various objects within large-scale three-dimensional tunnel point clouds, with a particular emphasis on achieving precise segmentation in the context of seepage flow [45]. This article innovatively projects points to a cylinder and utilizes 2DCNN to segment components such as power tracks, cables, and pipes within the tunnel.…”

Section: Point Cloud Segmentation In Tunnelmentioning

confidence: 99%

A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model

Kang,

Chen,

et al. 2023

Remote Sensing

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In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and insufficient annotated data, contribute to the subpar performance of conventional point cloud segmentation algorithms. To address this issue, a 3D point cloud segmentation framework specifically for underground tunnels is proposed based on the Segment Anything Model (SAM). This framework effectively leverages the generalization capability of the visual foundation model to automatically adapt to various scenes and perform efficient segmentation of tunnel point clouds. Specifically, the tunnel is first sliced along its direction on the tunnel line. Then, each sliced point cloud is projected onto a two-dimensional plane. Various projection methods and point cloud coloring techniques are employed to enhance SAM’s segmentation performance in images. Finally, the semantic segmentation of the entire underground tunnel is achieved by a small set of manually annotated semantic labels used as prompts in a progressive and recursive manner. The key feature of this method lies in its independence from model training, as it directly and efficiently addresses tunnel point cloud segmentation challenges by capitalizing on the generalization capability of foundation model. Comparative experiments against classical region growing algorithms and PointNet++ deep learning algorithms demonstrate the superior performance of our proposed algorithm.

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Section: Point Cloud Segmentation In Tunnelmentioning

confidence: 99%