Splats in Splats: Embedding Invisible 3D Watermark within Gaussian Splatting

• URL: http://arxiv.org/abs/2412.03121v1
• Date: Wed, 04 Dec 2024 08:40:11 GMT
• Title: Splats in Splats: Embedding Invisible 3D Watermark within Gaussian Splatting
• Authors: Yijia Guo, Wenkai Huang, Yang Li, Gaolei Li, Hang Zhang, Liwen Hu, Jianhua Li, Tiejun Huang, Lei Ma,
• Abstract summary: WaterGS is the first 3DGS watermarking framework that embeds 3D content in 3DGS itself without modifying any attributes of the vanilla 3DGS. Tests indicate that WaterGS significantly outperforms existing 3D steganography techniques, with 5.31% higher scene fidelity and 3X faster rendering speed.
• Score: 28.790625685438677
• License:
• Abstract: 3D Gaussian splatting (3DGS) has demonstrated impressive 3D reconstruction performance with explicit scene representations. Given the widespread application of 3DGS in 3D reconstruction and generation tasks, there is an urgent need to protect the copyright of 3DGS assets. However, existing copyright protection techniques for 3DGS overlook the usability of 3D assets, posing challenges for practical deployment. Here we describe WaterGS, the first 3DGS watermarking framework that embeds 3D content in 3DGS itself without modifying any attributes of the vanilla 3DGS. To achieve this, we take a deep insight into spherical harmonics (SH) and devise an importance-graded SH coefficient encryption strategy to embed the hidden SH coefficients. Furthermore, we employ a convolutional autoencoder to establish a mapping between the original Gaussian primitives' opacity and the hidden Gaussian primitives' opacity. Extensive experiments indicate that WaterGS significantly outperforms existing 3D steganography techniques, with 5.31% higher scene fidelity and 3X faster rendering speed, while ensuring security, robustness, and user experience. Codes and data will be released at https://water-gs.github.io.

Related papers

• CLIP-GS: Unifying Vision-Language Representation with 3D Gaussian Splatting [88.24743308058441]
  We present CLIP-GS, a novel multimodal representation learning framework grounded in 3DGS. We develop an efficient way to generate triplets of 3DGS, images, and text, facilitating CLIP-GS in learning unified multimodal representations.
  arXiv  Detail & Related papers  (2024-12-26T09:54:25Z)
• 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)
• GuardSplat: Efficient and Robust Watermarking for 3D Gaussian Splatting [70.81218231206617]
  3D Gaussian Splatting (3DGS) has recently created impressive assets for various applications. Existing watermarking methods are not suited for 3DGS considering security, capacity, and invisibility. We propose GuardSplat, an innovative and efficient framework that effectively protects the copyright of 3DGS assets.
  arXiv  Detail & Related papers  (2024-11-29T17:59:03Z)
• 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)
• WildGaussians: 3D Gaussian Splatting in the Wild [80.5209105383932]
  We introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data.
  arXiv  Detail & Related papers  (2024-07-11T12:41:32Z)
• GS-Hider: Hiding Messages into 3D Gaussian Splatting [18.16759704305008]
  3D Gaussian Splatting (3DGS) has already become the emerging research focus in the fields of 3D scene reconstruction and novel view synthesis. It is crucial to protect the copyright, integrity, and privacy of such 3D assets. We propose a steganography framework for 3DGS, dubbed GS-Hider, which can embed 3D scenes and images into original GS point clouds.
  arXiv  Detail & Related papers  (2024-05-24T00:18:15Z)
• DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus [56.45194233357833]
  We propose DoGaussian, a method that trains 3DGS distributedly. Our method accelerates the training of 3DGS by 6+ times when evaluated on large-scale scenes.
  arXiv  Detail & Related papers  (2024-05-22T19:17:58Z)
• SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition [66.56357905500512]
  3D Gaussian Splatting has emerged as an alternative 3D representation for novel view synthesis. We propose SAGD, a conceptually simple yet effective boundary-enhanced segmentation pipeline for 3D-GS. Our approach achieves high-quality 3D segmentation without rough boundary issues, which can be easily applied to other scene editing tasks.
  arXiv  Detail & Related papers  (2024-01-31T14:19:03Z)
• Text-to-3D using Gaussian Splatting [18.163413810199234]
  This paper proposes GSGEN, a novel method that adopts Gaussian Splatting, a recent state-of-the-art representation, to text-to-3D generation. GSGEN aims at generating high-quality 3D objects and addressing existing shortcomings by exploiting the explicit nature of Gaussian Splatting. Our approach can generate 3D assets with delicate details and accurate geometry.
  arXiv  Detail & Related papers  (2023-09-28T16:44:31Z)

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.