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 | Conference PDF | Archive PDF | Plain Text | 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. |