Fast Feedforward 3D Gaussian Splatting Compression
Authors: Yihang Chen, Qianyi Wu, Mengyao Li, Weiyao Lin, Mehrtash Harandi, Jianfei Cai
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across various datasets demonstrate the effectiveness of FCGS, achieving a compression ratio of 20 while maintaining excellent fidelity, even surpassing most of the optimization-based methods. |
| Researcher Affiliation | Academia | Yihang Chen1,2, Qianyi Wu2 , Mengyao Li3,2, Weiyao Lin1 , Mehrtash Harandi2, Jianfei Cai2 1Shanghai Jiao Tong University, 2Monash University, 3Shanghai University |
| Pseudocode | No | The paper describes the method using textual descriptions and architectural diagrams (Figure 2, Figure 3), but no explicitly labeled 'Pseudocode' or 'Algorithm' block is present. |
| Open Source Code | Yes | Code: github.com/Yihang Chen-ee/FCGS. |
| Open Datasets | Yes | To achieve that, we refer to DL3DV dataset (Ling et al., 2024), which contains approximately 7K multi-view scenes. ... For 3DGS from optimization, we employ DL3DV-GS, Mip Ne RF360 (Barron et al., 2022), and Tank&Temples (Knapitsch et al., 2017) for evaluation. ... We utilize 10 scenes from ACID (Liu et al., 2021) and 50 scenes from Gobjaverse (Qiu et al., 2023; Deitke et al., 2022) for these two models for evaluation. |
| Dataset Splits | Yes | After filtering out low-quality ones, we obtain 6770 3DGS, and randomly split 100 for testing and the remaining for training. This dataset is referred to as DL3DV-GS. |
| Hardware Specification | Yes | Our FCGS model is implemented using the Py Torch framework (Paszke et al., 2019) and trained on a single NVIDIA L40s GPU. |
| Software Dependencies | No | Our FCGS model is implemented using the Py Torch framework (Paszke et al., 2019) and trained on a single NVIDIA L40s GPU. No specific version number for PyTorch or other software dependencies is provided. |
| Experiment Setup | Yes | The dimension of ˆy is set to 256 for color (m = 1). For ˆz, dimensions are set to 16, 24, and 64 for geometry, color (m = 0), and color (m = 1), respectively. Grid resolutions are {70, 80, 90} for 3D grids and {300, 400, 500} for 2D grids. We set N s to 4, using uneven splitting ratios of { 1 3}, with uniform random sampling. m is set to 0.01. In inference, we maintain a same random seed in encoding and decoding to guarantee consistency. The training batch size is 1 (i.e., one 3DGS scene per training step). We adjust λ from 1e 4 to 16e 4 to achieve variable bitrates. |