3D-GSW: 3D Gaussian Splatting for Robust Watermarking

• URL: http://arxiv.org/abs/2409.13222v3
• Date: Mon, 23 Dec 2024 09:14:23 GMT
• Title: 3D-GSW: 3D Gaussian Splatting for Robust Watermarking
• Authors: Youngdong Jang, Hyunje Park, Feng Yang, Heeju Ko, Euijin Choo, Sangpil Kim,
• Abstract summary: We introduce a robust watermarking method for 3D-GS that secures ownership of both the model and its rendered images. Our proposed method remains robust against distortions in rendered images and model attacks while maintaining high rendering quality.
• Score: 5.52538716292462
• License:
• Abstract: As 3D Gaussian Splatting~(3D-GS) gains significant attention and its commercial usage increases, the need for watermarking technologies to prevent unauthorized use of the 3D-GS models and rendered images has become increasingly important. In this paper, we introduce a robust watermarking method for 3D-GS that secures ownership of both the model and its rendered images. Our proposed method remains robust against distortions in rendered images and model attacks while maintaining high rendering quality. To achieve these objectives, we present Frequency-Guided Densification~(FGD), which removes 3D Gaussians based on their contribution to rendering quality, enhancing real-time rendering and the robustness of the message. FGD utilizes Discrete Fourier Transform to split 3D Gaussians in high-frequency areas, improving rendering quality. Furthermore, we employ a gradient mask for 3D Gaussians and design a wavelet-subband loss to enhance rendering quality. Our experiments show that our method embeds the message in the rendered images invisibly and robustly against various attacks, including model distortion. Our method achieves state-of-the-art performance. Project page: https://kuai-lab.github.io/3dgsw2024/

Related papers

• WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking [44.335142946449245]
  WATER-GS is a novel method designed to protect 3DGS copyrights through a universal watermarking strategy. We introduce a pre-trained watermark decoder, treating raw 3DGS generative modules as potential watermarks to ensure imperceptibility. We implement novel 3D distortion layers to enhance the robustness of the embedded watermark against common real-world distortions of point cloud data.
  arXiv  Detail & Related papers  (2024-12-07T16:44:22Z)
• Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels [51.08794269211701]
  We introduce 3D Linear Splatting (3DLS), which replaces Gaussian kernels with linear kernels to achieve sharper and more precise results. 3DLS demonstrates state-of-the-art fidelity and accuracy, along with a 30% FPS improvement over baseline 3DGS.
  arXiv  Detail & Related papers  (2024-11-19T11:59:54Z)
• GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting [41.90891053671943]
  Digital watermarking techniques can be applied to embed ownership information discreetly within 3DGS models. Naively embedding the watermark on a pre-trained 3DGS can cause obvious distortion in rendered images. We propose an uncertainty-based method that constrains the perturbation of model parameters to achieve invisible watermarking for 3DGS.
  arXiv  Detail & Related papers  (2024-10-31T08:08:54Z)
• EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis [72.53316783628803]
  We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time differentiable emission-only volume rendering. Unlike recentization based approach by 3D Gaussian Splatting (3DGS), our primitive based representation allows for exact volume rendering. We show that our method is more accurate with blending issues than 3DGS and follow-up work on view rendering.
  arXiv  Detail & Related papers  (2024-10-02T17:59:09Z)
• ScalingGaussian: Enhancing 3D Content Creation with Generative Gaussian Splatting [30.99112626706754]
  The creation of high-quality 3D assets is paramount for applications in digital heritage, entertainment, and robotics. Traditionally, this process necessitates skilled professionals and specialized software for modeling. We introduce a novel 3D content creation framework, which generates 3D textures efficiently.
  arXiv  Detail & Related papers  (2024-07-26T18:26:01Z)
• GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction [52.04103235260539]
  We present a diffusion model approach based on Gaussian Splatting representation for 3D object reconstruction from a single view. The model learns to generate 3D objects represented by sets of GS ellipsoids. The final reconstructed objects explicitly come with high-quality 3D structure and texture, and can be efficiently rendered in arbitrary views.
  arXiv  Detail & Related papers  (2024-07-05T03:43:08Z)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios. We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• 3D-HGS: 3D Half-Gaussian Splatting [5.766096863155448]
  Photo-realistic 3D Reconstruction is a fundamental problem in 3D computer vision. We propose to employ 3D Half-Gaussian (3D-HGS) kernels, which can be used as a plug-and-play kernel.
  arXiv  Detail & Related papers  (2024-06-04T19:04:29Z)
• Mani-GS: Gaussian Splatting Manipulation with Triangular Mesh [44.57625460339714]
  We propose a triangular mesh to manipulate 3DGS directly with self-adaptation. Our approach is capable of handling large deformations, local manipulations, and soft body simulations while keeping high-fidelity rendering.
  arXiv  Detail & Related papers  (2024-05-28T04:13:21Z)
• Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting [55.71424195454963]
  Spec-Gaussian is an approach that utilizes an anisotropic spherical Gaussian appearance field instead of spherical harmonics. Our experimental results demonstrate that our method surpasses existing approaches in terms of rendering quality. This improvement extends the applicability of 3D GS to handle intricate scenarios with specular and anisotropic surfaces.
  arXiv  Detail & Related papers  (2024-02-24T17:22:15Z)
• AGG: Amortized Generative 3D Gaussians for Single Image to 3D [108.38567665695027]
  We introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly produces 3D Gaussians from a single image. AGG decomposes the generation of 3D Gaussian locations and other appearance attributes for joint optimization. We propose a cascaded pipeline that first generates a coarse representation of the 3D data and later upsamples it with a 3D Gaussian super-resolution module.
  arXiv  Detail & Related papers  (2024-01-08T18:56:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.