Combination of Images and Point Clouds in a Generative Adversarial Network for Upsampling Crack Point Clouds

Abstract

Point cloud data of cracks can be used for various purposes such as crack detection, depth calculation and crack segmentation. Upsampling low-density point clouds can help to improve the performance of those tasks. Building on existing methods that upsample point clouds from low-resolution point cloud input, to improve feature definition, this paper proposes a new method for upsampling low-density point clouds using a combination of these point clouds and corresponding 2D images of the original objects as input data. We use an architecture based on Generative Adversarial Networks (GAN) for training input point clouds with additional information from the corresponding 2D images. The key idea is to exploit features from both 2D images and point clouds to enrich point clouds in both the training and testing phases. Our method takes advantage of the combination of 2D images and point clouds using a GAN framework. Experimental results show our proposed method achieves a higher effectiveness compared with previous upsampling methods.