Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | 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. |