DiffGS: Functional Gaussian Splatting Diffusion
Authors: Junsheng Zhou, Weiqi Zhang, Yu-Shen Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 {zhou-js24,zwq23}@mails.tsinghua.edu.cn liuyushen@tsinghua.edu.cn |
| 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. |