Sequence Matters: Harnessing Video Models in 3D Super-Resolution
Hyun-kyu Ko, Dongheok Park, Youngin Park, Byeonghyeon Lee, Juhee Han, Eunbyung Park
2024-12-23
Summary
This paper talks about using video models to improve the process of creating high-quality 3D models from low-resolution images. It focuses on how the sequence of images matters in achieving better results in 3D super-resolution.
What's the problem?
Creating detailed 3D models from low-resolution images is difficult because traditional methods often look at each image independently. This can lead to inconsistencies between the images, resulting in a final model that doesn't look good or realistic. Existing techniques to fix these issues have not fully solved the problem.
What's the solution?
The authors propose a new approach that leverages video super-resolution (VSR) models, which consider multiple images at once to maintain consistency. By using VSR models, they can reference surrounding images to create more accurate and detailed 3D models. They also introduce a method to align the low-resolution images without needing complex adjustments, making the process simpler and more efficient. Their experiments show that this approach achieves state-of-the-art results on standard datasets for 3D super-resolution.
Why it matters?
This research is important because it enhances the ability to create high-quality 3D models from lower-quality images, which is useful in various fields like gaming, virtual reality, and film production. By improving the accuracy and detail of these models, it can lead to better visual experiences and applications.
Abstract
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