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..
DiffGS: Functional Gaussian Splatting Diffusion
Authors: Junsheng Zhou, Weiqi Zhang, Yu-Shen Liu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiment For unconditional generation of 3D Gaussian Splatting, we conduct experiments under the airplane and chair classes of Shape Net [6] dataset. Following previous works [42, 4], we report two widely-used image generation metrics Fréchet Inception Distance (FID) [20] and Kernel Inception Distance (KID) [3] for evaluating the rendering quality of our proposed Diff GS and previous state-of-the-art works. |
| Researcher Affiliation | Academia | Junsheng Zhou Weiqi Zhang Yu-Shen Liu School of Software, Tsinghua University, Beijing, China EMAIL EMAIL |
| Pseudocode | No | The paper describes algorithms such as the Gaussian Extraction Algorithm in Section 3.4, but it does not present them in a structured pseudocode block or a clearly labeled algorithm figure. |
| Open Source Code | Yes | We provide our demonstration code as a part of our supplementary materials. We will release the source code, data and instructions upon acceptance. |
| Open Datasets | Yes | For unconditional generation of 3D Gaussian Splatting, we conduct experiments under the airplane and chair classes of Shape Net [6] dataset. |
| Dataset Splits | No | The paper mentions splitting the dataset into train/test sets for Shape Net ('we split the airplane and chair classes of the Shape Net dataset into train/test sets') but does not explicitly provide details about a validation split, its percentages, or counts. |
| Hardware Specification | Yes | Inference time is measured on a single NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch Lightning' for implementation and 'Adam optimizer' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We leverage the Adam optimizer with a learning rate of 0.0001. |