Sequence Matters: Harnessing Video Models in 3D Super-Resolution
Authors: name: Hyun-kyu Ko, name: Dongheok Park, name: Youngin Park, name: Byeonghyeon Lee, name: Juhee Han, name: Eunbyung Park

3D super-resolution aims to reconstruct high-fidelity 3D models from
low-resolution (LR) multi-view images. Early studies primarily focused on
single-image super-resolution (SISR) models to upsample LR images into
high-resolution images. However, these methods often lack view consistency
because they operate independently on each image. Although various
post-processing techniques have been extensively explored to mitigate these
inconsistencies, they have yet to fully resolve the issues. In this paper, we
perform a comprehensive study of 3D super-resolution by leveraging video
super-resolution (VSR) models. By utilizing VSR models, we ensure a higher
degree of spatial consistency and can reference surrounding spatial
information, leading to more accurate and detailed reconstructions. Our
findings reveal that VSR models can perform remarkably well even on sequences
that lack precise spatial alignment. Given this observation, we propose a
simple yet practical approach to align LR images without involving fine-tuning
or generating 'smooth' trajectory from the trained 3D models over LR images.
The experimental results show that the surprisingly simple algorithms can
achieve the state-of-the-art results of 3D super-resolution tasks on standard
benchmark datasets, such as the NeRF-synthetic and MipNeRF-360 datasets.
Project page: https://ko-lani.github.io/Sequence-Matters