Generative Adversarial Networks-based Privacy-Preserving 3D Reconstruction

A large-scale image collection is crucial to the success of 3D reconstruction. Crowdsourcing, as a new pattern, can be utilized to collect high-quality images in an efficient way. However, the sensitive information in images may be exposed during the image transmission process. The general privacy policies perhaps will cause the loss or change of critical information, which may give rise to a decline in the performance of 3D reconstruction. Hence, how to achieve image privacy-preserving while guaranteeing to reconstruct a complete 3D model is important and significant. In this paper, we propose PicPrivacy to address this problem, which consists of three parts. (1) Using a pre-trained deep convolution neural network to segment sensitive information and erase it from images. (2) Using a GAN-based image feature completion algorithm to repair blank regions and minimize the absolute information gap between generated images and raw ones. (3) Taking generated images as the input of 3D reconstruction and using a structure-from-motion algorithm to reconstruct 3D models. Finally, we extensively evaluate the performance of PicPrivacy on realworld datasets. The results demonstrate that PicPrivacy not only achieves individual privacy-preserving but also can guarantee to create complete 3D models.