  Computer vision algorithms have been prevalently utilized for 3-D road
imaging and pothole detection for over two decades. Nonetheless, there is a
lack of systematic survey articles on state-of-the-art (SoTA) computer vision
techniques, especially deep learning models, developed to tackle these
problems. This article first introduces the sensing systems employed for 2-D
and 3-D road data acquisition, including camera(s), laser scanners, and
Microsoft Kinect. Afterward, it thoroughly and comprehensively reviews the SoTA
computer vision algorithms, including (1) classical 2-D image processing, (2)
3-D point cloud modeling and segmentation, and (3) machine/deep learning,
developed for road pothole detection. This article also discusses the existing
challenges and future development trends of computer vision-based road pothole
detection approaches: classical 2-D image processing-based and 3-D point cloud
modeling and segmentation-based approaches have already become history; and
Convolutional neural networks (CNNs) have demonstrated compelling road pothole
detection results and are promising to break the bottleneck with the future
advances in self/un-supervised learning for multi-modal semantic segmentation.
We believe that this survey can serve as practical guidance for developing the
next-generation road condition assessment systems.
