On Scaling Up 3D Gaussian Splatting Training
Authors: Hexu Zhao, Haoyang Weng, Daohan Lu, Ang Li, Jinyang Li, Aurojit Panda, Saining Xie
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations using large-scale, high-resolution scenes show that Grendel enhances rendering quality by scaling up 3DGS parameters across multiple GPUs. On the 4K Rubble dataset, we achieve a test PSNR of 27.28 by distributing 40.4 million Gaussians across 16 GPUs, compared to a PSNR of 26.28 using 11.2 million Gaussians on a single GPU. |
| Researcher Affiliation | Academia | Hexu Zhao1, Haoyang Weng1 , Daohan Lu1 , Ang Li2, Jinyang Li1, Aurojit Panda1, Saining Xie1 1New York University 2Pacific Northwest National Laboratory |
| Pseudocode | Yes | We show the pseudocode (Algorithm 1) for calculating the Division Points to split an image into load-balanced subsequences of blocks. |
| Open Source Code | Yes | Grendel is an open-source project available at: https://github.com/nyu-systems/Grendel-GS |
| Open Datasets | Yes | On the 4K Rubble dataset, we achieve a test PSNR of 27.28 by distributing 40.4 million Gaussians across 16 GPUs... Dataset: Rubble Resolution: 4K #Gaussians: 40,000,000 Training: 16 GPUs/BS=16... The standard Rubble dataset (Turki et al., 2022) contains 1657 images... Matrix City Block_All (Li et al., 2023)... Tanks & Temple (Knapitsch et al., 2017)... Deep Blending (Hedman et al., 2018)... Mip-Ne RF 360 (Barron et al., 2022) |
| Dataset Splits | Yes | Table 1: Scenes used in our evaluation: We cover scenes of varying sizes and resolutions. Tanks & Temple (Knapitsch et al., 2017) ... Test Set Setting: 1/8 of all images Deep Blending (Hedman et al., 2018) ... Test Set Setting: 1/8 of all images Mip-Ne RF 360 (Barron et al., 2022) ... Test Set Setting: 1/8 of all images Rubble (Turki et al., 2022) ... Test Set Setting: official test set Matrix City Block_All (Li et al., 2023) ... Test Set Setting: official test set |
| Hardware Specification | Yes | Experimental Setup. We conducted our evaluation in the Perlmutter GPU cluster NERSC. Each node we used was equipped with 4 A100 GPUs with 40GB of GPU memory, and interconnected with each other using 25GB/s NVLink per direction. Servers were connected to each other using a 200Gbps Slingshot network. |
| Software Dependencies | No | The paper does not explicitly provide specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We explore various optimization hyperparameter scaling strategies and find that a simple sqrt(batch_size) scaling rule is highly effective... To maintain data efficiency and reconstruction quality with larger batches, one needs to re-tune optimizer hyperparameters. To this end, we introduce an automatic hyperparameter scaling rule for batched 3DGS training based on a heuristical independent gradients hypothesis... Table 4: Scalablity on Rubble: Gaussian Quantity, Results and Hyperparameter Settings... Table 5: Matrix City Block_All Statistics: Gaussian Quantity, Results and Hyperparameter Settings... Table 6: Bicycle Statistics: Gaussian Quantity, Results and Hyperparameter settings |