Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction
This provides a unified evaluation approach for researchers and practitioners in computer vision and graphics, enabling easier benchmarking and acquisition planning without reliance on geometric accuracy, though it is incremental as it builds on existing view synthesis concepts.
The paper tackles the problem of evaluating visual quality in 3D reconstruction and rendering systems, proposing a novel view prediction error metric that does not require ground truth geometry, and demonstrates its utility across various methods including geometry-based and image-based techniques.
The ultimate goal of many image-based modeling systems is to render photo-realistic novel views of a scene without visible artifacts. Existing evaluation metrics and benchmarks focus mainly on the geometric accuracy of the reconstructed model, which is, however, a poor predictor of visual accuracy. Furthermore, using only geometric accuracy by itself does not allow evaluating systems that either lack a geometric scene representation or utilize coarse proxy geometry. Examples include light field or image-based rendering systems. We propose a unified evaluation approach based on novel view prediction error that is able to analyze the visual quality of any method that can render novel views from input images. One of the key advantages of this approach is that it does not require ground truth geometry. This dramatically simplifies the creation of test datasets and benchmarks. It also allows us to evaluate the quality of an unknown scene during the acquisition and reconstruction process, which is useful for acquisition planning. We evaluate our approach on a range of methods including standard geometry-plus-texture pipelines as well as image-based rendering techniques, compare it to existing geometry-based benchmarks, and demonstrate its utility for a range of use cases.