GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
Authors: Xiufeng Huang, Ruiqi Li, Yiu-ming Cheung, Ka Chun Cheung, Simon See, Renjie Wan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on the Blender, LLFF, and Mip Ne RF-360 datasets to validate the effectiveness of our proposed method, demonstrating state-of-the-art performance on both message decoding accuracy and view synthesis quality. |
| Researcher Affiliation | Collaboration | Xiufeng Huang1,2, Ruiqi Li1, Yiu-ming Cheung1, Ka Chun Cheung2, Simon See2, Renjie Wan1 1 Department of Computer Science, Hong Kong Baptist University 2 NVIDIA AI Technology Center |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project page: https://kevinhuangxf.github.io/Gaussian Marker. |
| Open Datasets | Yes | Dataset. We use three benchmark datasets for evaluation: Blender [7] (8 detailed synthetic objects), LLFF [53] (9 real-world scenes), and Mip-Ne RF360 [54] (9 real-world scenes). |
| Dataset Splits | Yes | For Blender [7], we directly follow the dataset splitting to use 100 viewpoints for training and 200 views for testing. For LLFF [53], we follow the dataset splitting in Ne RF [7]. In general, 1/8 images in each scene are used for testing and others for training. For Mip-Ne RF360 [54], we use a train/test split suggested by Mip-Ne RF360, taking every 8th photo for testing and others for training. |
| Hardware Specification | Yes | The training takes 1000 (Blender, LLFF) or 2000 steps (Mip Ne RF360) and can finish within 20 minutes using a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer [24]' but does not provide specific version numbers for any programming languages (e.g., Python), deep learning frameworks (e.g., PyTorch, TensorFlow), or other key software libraries used in the experiments. |
| Experiment Setup | Yes | We use the Adam optimizer [24] to optimize the 3D message decoder Dψ and classifier Dξ with default values β1 = 0.9, β2 = 0.999, ϵ = 10 8, and a learning rate 1 10 4 that decays following the exponential scheduler during optimization. We set λ1 = 10.0, λ2 = 1.0 for 2D watermarking loss L2D and λ 1 = 2.0, λ 2 = 1.0 for 3D watermarking loss L3D to adapt the training losses. |