MVCrackViT: Robust Multi-View Crack Detection For Point Cloud Segmentation Using View Attention

Abstract

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Adapting computer vision algorithms for inspecting civil structures brings significant societal benefits. Images captured from civil structures often exhibit distinct overlap, typically to perform 3D reconstruction. In this work, the potential of multiple overlapping views is harnessed for robust multi-view crack detection. A transformer approach, named MVCrackViT, is designed to use attention over multiple views, enabling point cloud crack segmentation from the views directly. To address quality issues such as motion blur, defocus, and low exposure commonly found in real-world data, artificial view corruption is applied to accomplish training from image data alone. With reasonable positional tolerance, a performance of approximately 90% clCloudIoU is achieved on a 3D crack dataset, the first of its kind. The powerful clCloudIoU metric is introduced to evaluate crack detection in 3D space.