Sequence Matters: Harnessing Video Models in 3D Super-Resolution
Authors: Hyun-kyu Ko, Dongheok Park, Youngin Park, Byeonghyeon Lee, Juhee Han, Eunbyung Park
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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 Ne RF-synthetic and Mip Ne RF-360 datasets. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, Sungkyunkwan University 2Department of Artificial Intelligence, Sungkyunkwan University 3Visual Display Division, Samsung Electorics |
| Pseudocode | Yes | Algorithm 1: A Simple Greedy Algorithm Input: A set of unordered images, I = {Ij}N j=1 Output: An ordered sequence of images, S |
| Open Source Code | Yes | Project Page https://ko-lani.github.io/Sequence-Matters |
| Open Datasets | Yes | Datasets We use the Ne RF Synthetic Blender dataset (Mildenhall et al. 2021) and the Mip-Ne RF 360 dataset (Barron et al. 2022). |
| Dataset Splits | No | The paper mentions using the Ne RF Synthetic Blender dataset and the Mip-Ne RF 360 dataset and downsampling them, but it does not specify the training/test/validation splits or percentages used for experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "the open-source 3D Gaussian Splatting code base" and "PSRT" as a VSR backbone, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Following the 3DGS protocol, we train both coarse and fine 3DGS models for 30,000 iterations. To create the low-resolution (LR) dataset, we downsample the high-resolution (HR) dataset using bicubic interpolation with a downscale factor of 4. |