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..
2D Gaussian Splatting for Outdoor Scene Decomposition and Relighting
Authors: Wei Feng, Kangrui Ye, Qi Zhang, Qian Zhang, Nan Li
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple challenging outdoor datasets validate the effectiveness of OSDR-GS, which achieves the state-of-the-art performance in changing lighting scene inverse rendering. |
| Researcher Affiliation | Academia | Wei Feng , Kangrui Ye , Qi Zhang , Qian Zhang and Nan Li Tianjin University EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical equations and descriptive text, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate our OSDR-GS with SOTA baselines on both Ne RF-OSR [Rudnev et al., 2022] and Mip-Ne RF 360 [Barron et al., 2022] dataset. |
| Dataset Splits | Yes | Following GS-IR [Liang et al., 2024], we use images downsampled by a factor of 4, and pick every eighth image as a test image for dataset splitting. |
| Hardware Specification | Yes | For each scene, training for 30k iterations on a single NVIDIA RTX 4090 GPU takes approximately 30 minutes. |
| Software Dependencies | No | The paper references various existing techniques and models (e.g., 2DGS, NeRF, 3DGS) but does not provide specific version numbers for the software dependencies used in its own implementation. |
| Experiment Setup | Yes | L = Lc + λ1Ln + λ2Lnn + λ3Lbin, (16) where λ1 = 0.05, λ2 = 0.01 and λ3 = 0.001 are predefined weighting hyperparameters for each loss terms. For each scene, training for 30k iterations on a single NVIDIA RTX 4090 GPU takes approximately 30 minutes. |