Puzzle Similarity: A Perceptually-guided Cross-Reference Metric for Artifact Detection in 3D Scene Reconstructions
This addresses the challenge of assessing reconstruction quality without ground truth images, enabling applications like automatic image restoration and guided acquisition.
The paper tackles the problem of automatically detecting artifacts in novel views of 3D scene reconstructions by proposing a new cross-reference metric called Puzzle Similarity, which achieves state-of-the-art localization of artifacts, correlating with human assessment on a novel human-labeled dataset.
Modern reconstruction techniques can effectively model complex 3D scenes from sparse 2D views. However, automatically assessing the quality of novel views and identifying artifacts is challenging due to the lack of ground truth images and the limitations of no-reference image metrics in predicting reliable artifact maps. The absence of such metrics hinders assessment of the quality of novel views and limits the adoption of post-processing techniques, such as inpainting, to enhance reconstruction quality. To tackle this, recent work has established a new category of metrics (cross-reference), predicting image quality solely by leveraging context from alternate viewpoint captures (arXiv:2404.14409). In this work, we propose a new cross-reference metric, Puzzle Similarity, which is designed to localize artifacts in novel views. Our approach utilizes image patch statistics from the training views to establish a scene-specific distribution, later used to identify poorly reconstructed regions in the novel views. Given the lack of good measures to evaluate cross-reference methods in the context of 3D reconstruction, we collected a novel human-labeled dataset of artifact and distortion maps in unseen reconstructed views. Through this dataset, we demonstrate that our method achieves state-of-the-art localization of artifacts in novel views, correlating with human assessment, even without aligned references. We can leverage our new metric to enhance applications like automatic image restoration, guided acquisition, or 3D reconstruction from sparse inputs. Find the project page at https://nihermann.github.io/puzzlesim/ .