3D Gaussian Rendering Can Be Sparser: Efficient Rendering via Learned Fragment Pruning
Authors: Zhifan Ye, Chenxi Wan, Chaojian Li, Jihoon Hong, Sixu Li, Leshu Li, Yongan Zhang, Yingyan (Celine) Lin
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments in both static and dynamic scenes validate the effectiveness of our approach. |
| Researcher Affiliation | Academia | Zhifan Ye, Chenxi Wan, Chaojian Li, Jihoon Hong, Sixu Li, Leshu Li, Yongan Zhang, Yingyan (Celine) Lin Georgia Institute of Technology {zye327, celine.lin}@gatech.edu |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found. |
| Open Source Code | Yes | Our code is available at https://github.com/GATECH-EIC/Fragment-Pruning. |
| Open Datasets | Yes | For static scenes, we adopt the five outdoor scenes and four indoor scenes from the Mip-Ne RF 360 dataset [6], two scenes ( Train and Truck ) from the Tanks&Temples dataset [19] and two scenes ( Dr Johnson and Playroom ) from the Deep Blending dataset [22]. For dynamic scenes, we select the Plenoptic Video Dataset [39], which is composed of six real-world video sequences. |
| Dataset Splits | No | The paper uses standard datasets but does not explicitly provide the training/validation/test dataset splits with proportions or specific methodologies for all experiments. Table 1 mentions 'test set' for measuring rendering time, but doesn't define the split. |
| Hardware Specification | Yes | To validate the effectiveness of the proposed approach, we benchmark the rendering speed of our method and the baselines on a consumer hardware device, Nvidia s edge GPU, the Jetson Orin NX [17]. |
| Software Dependencies | No | The paper mentions an 'Open GL-accelerated Gaussian Splatting renderer [38]' and uses 'Adam optimizer' and 'L1 Loss and SSIM Loss', but does not specify version numbers for Python, OpenGL, or any other software dependencies. |
| Experiment Setup | Yes | Specifically, we fine-tune each scene for 5,000 epochs, utilizing a batch size of 1. In particular, we adopt the Adam optimizer with a learning rate of 0.01, β1 = 0.9, and β2 = 0.99 during the fine-tuning process. We adopt the same L1 Loss and SSIM Loss as the pre-training process [16]. For dynamic scenes, we adjust our training batch size to 4, adhering to the default batch size as specified in the 4D Gaussian Splatting training [28]. |