3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors
Authors: Xi Liu, Chaoyi Zhou, Siyu Huang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on large-scale datasets of unbounded scenes demonstrate that 3DGS-Enhancer yields superior reconstruction performance and high-fidelity rendering results compared to state-of-the-art methods. The project webpage is https://xiliu8006.github.io/3DGS-Enhancer-project. |
| Researcher Affiliation | Academia | Xi Liu* Chaoyi Zhou* Siyu Huang Visual Computing Division School of Computing Clemson University {xi9, chaoyiz, siyuh}@clemson.edu |
| Pseudocode | No | The paper describes methods and processes but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and the generated dataset will be publicly available. The project webpage is https://xiliu8006.github.io/3DGS-Enhancer-project. |
| Open Datasets | Yes | In experiments, we generate large-scale datasets with pairs of low-quality and high-quality images on hundreds of unbounded scenes, based on DL3DV [21] |
| Dataset Splits | No | The paper specifies training and test sets but does not explicitly mention or detail a separate validation set or split for the experiments. |
| Hardware Specification | Yes | The training is conducted on 2 NVIDIA A100-80G GPUs over 3 days. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as programming languages (e.g., Python) or libraries (e.g., PyTorch, CUDA). |
| Experiment Setup | Yes | Our video diffusion model is fine-tuned with a learning rate of 0.0001, incorporating 500 steps for warm-up, followed by a total of 80,000 training steps. The batch size is set to 1 in each GPU, where each batch consisted of 25 images at 512x512 resolution. To optimize the training process, the Adam optimizer is employed. Additionally, a dropout rate of 0.1 is applied to the conditions between the first and last frames and the training process utilize CFG (classifier-free guidance) to train the diffusion model. The STD is fine-tuned with a learning rate of 0.0005 and 50,000 training steps. |