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..
3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors
Authors: Xi Liu, Chaoyi Zhou, Siyu Huang
NeurIPS 2024 | Venue PDF | 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 EMAIL |
| 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. |