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..
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS
Authors: Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, Dejia Xu, Zhangyang "Atlas" Wang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comparisons using the scene-scale view synthesis dataset provided by Mip-Ne RF360 [64], which comprises nine real-world large-scale scenes... We report metrics including the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and perceptual similarity as measured by LPIPS [66]. ...We performed ablation studies on each component of our method to evaluate their individual impacts. |
| Researcher Affiliation | Academia | Zhiwen Fan1 , Kevin Wang1 , Kairun Wen2, Zehao Zhu1, Dejia Xu1, Zhangyang Wang1 1The University of Texas at Austin 2XMU |
| Pseudocode | Yes | Algorithm 1 The overall pipeline of Light Gaussian |
| Open Source Code | No | We will release our code after our paper gets accepted. |
| Open Datasets | Yes | We conduct comparisons using the scene-scale view synthesis dataset provided by Mip-Ne RF360 [64], which comprises nine real-world large-scale scenes... In addition, we utilize the Tanks and Temples dataset [65]... The synthetic Blender dataset [5] includes eight photo-realistic synthetic objects with ground-truth controlled camera poses and rendered viewpoints (100 for training and 200 for testing). |
| Dataset Splits | No | The paper does not specify validation splits (percentages or counts) or cite predefined splits for any dataset. |
| Hardware Specification | Yes | All performance evaluations are conducted on an A6000 GPU. |
| Software Dependencies | No | Our framework is implemented in Py Torch and integrates the differentiable Gaussian rasterization technique from 3D-GS [16]. |
| Experiment Setup | Yes | In the Global Significance Calculation phase, we assign a power value of 0.1 in Eq. 4 and proceed to fine-tune the model for 5,000 steps during the Gaussian Co-adaptation process. For SH distillation, we downscale the 3-degree SHs to 2-degree, thereby reducing 21 elements for each Gaussian. This is further optimized by setting σ to 0.1 in the pseudo view synthesis stage. In the Gaussian VQ step, the codebook size is configured to 8192, selecting SHs with the least 60% significance score for the vector quantization (VQ ratio), to balance the trade-off between compression efficiency and fidelity. |