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..
R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction
Authors: Ruyi Zha, Tao Jun Lin, Yuanhao Cai, Jiwen Cao, Yanhao Zhang, Hongdong Li
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art approaches in accuracy and efficiency. |
| Researcher Affiliation | Academia | 1The Australian National University 2Johns Hopkins University 3Robotics Institute, University of Technology Sydney |
| Pseudocode | No | The paper describes algorithms in text but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and models are available on the project page https://github.com/Ruyi-Zha/r2_gaussian. |
| Open Datasets | Yes | For the synthetic dataset, we collect 15 real CT volumes... For real-world experiments, we use three cases from the FIPS dataset [56]... Refer to Appendix B for more details of datasets. (Appendix B: LIDC-IDRI [4] and Pancreas-CT [47], X-Plant [58], Sci Vis [26], FIPS [56]) |
| Dataset Splits | No | The paper discusses training and testing but does not explicitly provide training/test/validation dataset splits or refer to a validation set. |
| Hardware Specification | Yes | All methods run on a single RTX3090 GPU. |
| Software Dependencies | No | Our R2-Gaussian is implemented in Py Torch [44] and CUDA [50]... (no version numbers provided for reproducibility) |
| Experiment Setup | Yes | Learning rates for position, density, scale, and rotation are initially set as 0.0002, 0.01, 0.005, and 0.001, respectively, and exponentially to 0.1 of their initial values. Loss weights are λssim = 0.25 and λtv = 0.05. We initialize M = 50k Gaussians with a density threshold τ = 0.05 and scaling term k = 0.15. The TV volume size is D = 32. Adaptive control runs from 500 to 15k iterations with a gradient threshold of 0.00005. |