2D Gaussian Splatting for Outdoor Scene Decomposition and Relighting
Authors: Wei Feng, Kangrui Ye, Qi Zhang, Qian Zhang, Nan Li
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | 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. |