Recent advancements in large generative models and real-time neural rendering
using point-based techniques pave the way for a future of widespread visual
data distribution through sharing synthesized 3D assets. However, while
standardized methods for embedding proprietary or copyright information, either
overtly or subtly, exist for conventional visual content such as images and
videos, this issue remains unexplored for emerging generative 3D formats like
Gaussian Splatting. We present GaussianStego, a method for embedding
steganographic information in the rendering of generated 3D assets. Our
approach employs an optimization framework that enables the accurate extraction
of hidden information from images rendered using Gaussian assets derived from
large models, while maintaining their original visual quality. We conduct
preliminary evaluations of our method across several potential deployment
scenarios and discuss issues identified through analysis. GaussianStego
represents an initial exploration into the novel challenge of embedding
customizable, imperceptible, and recoverable information within the renders
produced by current 3D generative models, while ensuring minimal impact on the
rendered content's quality.

通过使用高斯细分图渲染生成的 3D 资产，将自定义、难以察觉和可恢复的信息嵌入当前 3D 生成模型生成的渲染内容中，从而实现在广泛分布的视觉数据共享中嵌入隐写信息的初步探索。

高斯隐写：生成 3D 高斯喷点的通用隐写流程

GaussianStego: A Generalizable Stenography Pipeline for Generative 3D  Gaussians Splatting

Recent advancements in real-time neural rendering using point-based
techniques have paved the way for the widespread adoption of 3D
representations. However, foundational approaches like 3D Gaussian Splatting
come with a substantial storage overhead caused by growing the SfM points to
millions, often demanding gigabyte-level disk space for a single unbounded
scene, posing significant scalability challenges and hindering the splatting
efficiency.
To address this challenge, we introduce LightGaussian, a novel method
designed to transform 3D Gaussians into a more efficient and compact format.
Drawing inspiration from the concept of Network Pruning, LightGaussian
identifies Gaussians that are insignificant in contributing to the scene
reconstruction and adopts a pruning and recovery process, effectively reducing
redundancy in Gaussian counts while preserving visual effects. Additionally,
LightGaussian employs distillation and pseudo-view augmentation to distill
spherical harmonics to a lower degree, allowing knowledge transfer to more
compact representations while maintaining reflectance. Furthermore, we propose
a hybrid scheme, VecTree Quantization, to quantize all attributes, resulting in
lower bitwidth representations with minimal accuracy losses.
In summary, LightGaussian achieves an averaged compression rate over 15x
while boosting the FPS from 139 to 215, enabling an efficient representation of
complex scenes on Mip-NeRF 360, Tank and Temple datasets.
Project website: this https URL

LightGaussian is a novel method for efficient 3D representation that reduces redundancy, distills spherical harmonics, and employs a hybrid quantization scheme, achieving high compression rates and improved FPS in complex scenes.