An Efficient, Fair, and Robust Image Pricing Mechanism for Crowdsourced 3D Reconstruction

Abstract

A large-scale and high-quality image collection is a fundamental demand in the 3D reconstruction scenario. Crowdsourcing can help us to collect lots of diversified images. However, it is difficult to attract people to accomplish tasks due to their self-interest. Besides, the quality of collected images is various. Low-quality images may degrade the performance of 3D reconstruction. To avoid low-quality images and motivate participants to provide high-quality images, we take image quality into account when allocating rewards. In this article, we propose a pricing mechanism, called ImgPricing, to determine the rewards of participants in 3D reconstruction. We model the process of image collection as a cooperative game, and regard image quality and the arrival sequence of images as critical factors in the reward allocation. ImgPricing differs from traditional pricing schemes, e.g., Shapley value and Banzhaf power index-based methods, in that it introduces the images’ arrival sequence to be an indispensable element. We lastly implement ImgPricing on the Android platform and extensively evaluate its performance. Our evaluation results demonstrate that ImgPricing outperforms other existing schemes in terms of computational efficiency, fairness, and robustness. In brief, our image quality-based pricing mechanism for crowdsourced 3D reconstruction is feasible and effective.