Evaluation of 3D Reconstruction Pipelines Under Varying Imaging Conditions

We present IVL-SYNTHSFM-v2, a dataset of 4000 images depicting five objects from different viewpoints and under different lighting conditions and acquisition setups. The images have been rendered from 3D scenes with varying camera positions, lights, camera depth of field, and motion blur. Images depict one of five objects each acquired using eight different acquisition setups (scenes). 100 images have been rendered from each scene. The dataset is intended to be used to evaluate 3D reconstruction algorithms. The varying imaging conditions are introduced to challenge the algorithms to address realistic, non-ideal situations. The dataset provides pixel-precise ground truth to perform accurate evaluations. We demonstrate the usefulness of IVL-SYNTHSFM-v2 by assessing state-of-the-art 3D reconstruction algorithms.