Benchmarking Large-Scale Multi-View 3D Reconstruction Using Realistic Synthetic Images

Benchmarking multi-view 3D reconstruction using synthetic images is a supplement for the traditional ways. The ground-truth data of synthetic benchmarks can be easily recorded in simulation and have a high accuracy. However, synthetic images from artificial scenes are usually lack of geometry and texture details. We propose a framework to build realistic synthetic benchmarks for real-world scenes. Real photos are also taken around the scenes. The key step of our approach is to transfer the image properties of the real photos to corresponding synthetic images, followed by a validation step to verify their similarity. Based on the proposed framework, we construct the Yungang Grotto dataset for large-scale multi-view 3D reconstruction, in which the largest scene is up to four thousand images, and the benchmarking results of a prevalent reconstruction pipeline on our dataset are also presented.