NOVA-3DGS: NO-reference objective VAlidation for 3D Gaussian Splatting

In recent years, radiance field methods, and in particular 3D Gaussian Splatting (3DGS), have distinguished themselves in the field of image-based rendering and scene reconstruction techniques, gaining significant success in academia and being cited in numerous research papers. Like other methods, 3DGS requires a large and diverse dataset of images for network training as a fundamental step to ensure effectiveness and high-quality results. Consequently, the acquisition phase is highly time-consuming, especially considering that a portion of the acquired dataset is not actually used for training but is reserved for testing. This is necessary because all commonly used metrics for evaluating the quality of 3D reconstructions, such as PSNR and SSIM, are reference-based metrics; i.e., requiring a ground truth. In this work, we present NOVA, a study focused on no-reference evaluation of 3DGS renders, based on key metrics in this field: PSNR and SSIM.