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..
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 | Venue PDF | 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 EMAIL |
| 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]. |