3D Gaussian Splatting (3DGS) has already become the emerging research focus
in the fields of 3D scene reconstruction and novel view synthesis. Given that
training a 3DGS requires a significant amount of time and computational cost,
it is crucial to protect the copyright, integrity, and privacy of such 3D
assets. Steganography, as a crucial technique for encrypted transmission and
copyright protection, has been extensively studied. However, it still lacks
profound exploration targeted at 3DGS. Unlike its predecessor NeRF, 3DGS
possesses two distinct features: 1) explicit 3D representation; and 2)
real-time rendering speeds. These characteristics result in the 3DGS point
cloud files being public and transparent, with each Gaussian point having a
clear physical significance. Therefore, ensuring the security and fidelity of
the original 3D scene while embedding information into the 3DGS point cloud
files is an extremely challenging task. To solve the above-mentioned issue, we
first propose a steganography framework for 3DGS, dubbed GS-Hider, which can
embed 3D scenes and images into original GS point clouds in an invisible manner
and accurately extract the hidden messages. Specifically, we design a coupled
secured feature attribute to replace the original 3DGS's spherical harmonics
coefficients and then use a scene decoder and a message decoder to disentangle
the original RGB scene and the hidden message. Extensive experiments
demonstrated that the proposed GS-Hider can effectively conceal multimodal
messages without compromising rendering quality and possesses exceptional
security, robustness, capacity, and flexibility. Our project is available at:
this https URL

提出了一种针对 3D 高斯喷洒的隐写术框架，名为 GS-Hider，可将 3D 场景和图像隐写到原始的 GS 点云中，且能准确提取隐藏的信息。

GS-Hider：将消息隐藏到三维高斯喷洒中

GS-Hider: Hiding Messages into 3D Gaussian Splatting

The Gaussian splatting for radiance field rendering method has recently
emerged as an efficient approach for accurate scene representation. It
optimizes the location, size, color, and shape of a cloud of 3D Gaussian
elements to visually match, after projection, or splatting, a set of given
images taken from various viewing directions. And yet, despite the proximity of
Gaussian elements to the shape boundaries, direct surface reconstruction of
objects in the scene is a challenge.
We propose a novel approach for surface reconstruction from Gaussian
splatting models. Rather than relying on the Gaussian elements' locations as a
prior for surface reconstruction, we leverage the superior novel-view synthesis
capabilities of 3DGS. To that end, we use the Gaussian splatting model to
render pairs of stereo-calibrated novel views from which we extract depth
profiles using a stereo matching method. We then combine the extracted RGB-D
images into a geometrically consistent surface. The resulting reconstruction is
more accurate and shows finer details when compared to other methods for
surface reconstruction from Gaussian splatting models, while requiring
significantly less compute time compared to other surface reconstruction
methods.
We performed extensive testing of the proposed method on in-the-wild scenes,
taken by a smartphone, showcasing its superior reconstruction abilities.
Additionally, we tested the proposed method on the Tanks and Temples benchmark,
and it has surpassed the current leading method for surface reconstruction from
Gaussian splatting models. Project page: this https URL

从高斯图形点渲染模型进行表面重构的方法能够产生更准确、更详细的重建结果，并且相较于其他方法，其计算时间更短。