CrossScore: Towards Multi-View Image Evaluation and Scoring
Keywords: Image quality assessment (IQA), Novel view synthesis (NVS), NeRF, Gaussian-Splatting
TL;DR: This method evaluates an image by comparing it with multiple views of the same scene through cross-attention, eliminating the need for a pre-aligned ground truth image.
Abstract: We introduce a novel Cross-Reference image quality assessment method that effectively fills the gap in the image assessment landscape, complementing the array of established evaluation schemes -- ranging from Full-Reference metrics like SSIM, No-Reference metrics such as NIQE, to General-Reference metrics including FID, and Multi-Modal-Reference metrics, e.g. CLIPScore.
Utilising a neural network with the cross-attention mechanism and a unique data collection pipeline from NVS optimisation, our method enables accurate image quality assessment without requiring ground truth references. By comparing a query image against multiple views of the same scene, our method addresses the limitations of existing metrics in novel view synthesis (NVS) and similar tasks where direct reference images are unavailable. Experimental results show that our method is closely correlated to the full-reference metric SSIM, while not requiring ground truth references.
***(Accpeted in ECCV 2024)***
Submission Number: 6
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