3D Gaussian Splatting (3DGS) has recently revolutionized radiance field
reconstruction, achieving high quality novel view synthesis and fast rendering
speed without baking. However, 3DGS fails to accurately represent surfaces due
to the multi-view inconsistent nature of 3D Gaussians. We present 2D Gaussian
Splatting (2DGS), a novel approach to model and reconstruct geometrically
accurate radiance fields from multi-view images. Our key idea is to collapse
the 3D volume into a set of 2D oriented planar Gaussian disks. Unlike 3D
Gaussians, 2D Gaussians provide view-consistent geometry while modeling
surfaces intrinsically. To accurately recover thin surfaces and achieve stable
optimization, we introduce a perspective-accurate 2D splatting process
utilizing ray-splat intersection and rasterization. Additionally, we
incorporate depth distortion and normal consistency terms to further enhance
the quality of the reconstructions. We demonstrate that our differentiable
renderer allows for noise-free and detailed geometry reconstruction while
maintaining competitive appearance quality, fast training speed, and real-time
rendering. Our code will be made publicly available.

通过将三维体积转化为一组二维定向平面高斯盘，使用透视准确的二维喷洒过程，结合深度扭曲和法线一致性项，实现从多视角图像中准确重建细表面并提高重建质量的一种方法。

二维高斯粒子扩散用于几何精确辐射场

2D Gaussian Splatting for Geometrically Accurate Radiance Fields

Implicit neural representations (INRs) recently achieved great success in
image representation and compression, offering high visual quality and fast
rendering speeds with 10-1000 FPS, assuming sufficient GPU resources are
available. However, this requirement often hinders their use on low-end devices
with limited memory. In response, we propose a groundbreaking paradigm of image
representation and compression by 2D Gaussian Splatting, named GaussianImage.
We first introduce 2D Gaussian to represent the image, where each Gaussian has
8 parameters including position, covariance and color. Subsequently, we unveil
a novel rendering algorithm based on accumulated summation. Remarkably, our
method with a minimum of 3$\times$ lower GPU memory usage and 5$\times$ faster
fitting time not only rivals INRs (e.g., WIRE, I-NGP) in representation
performance, but also delivers a faster rendering speed of 1500-2000 FPS
regardless of parameter size. Furthermore, we integrate existing vector
quantization technique to build an image codec. Experimental results
demonstrate that our codec attains rate-distortion performance comparable to
compression-based INRs such as COIN and COIN++, while facilitating decoding
speeds of approximately 1000 FPS. Additionally, preliminary proof of concept
shows that our codec surpasses COIN and COIN++ in performance when using
partial bits-back coding.

通过 2D 高斯扩散的方式，我们提出了一种开创性的图像表示和压缩范式，名为高斯图像。与 INRs 相比，我们的方法在表示性能上不逊色，并且具有最低 3 倍的 GPU 内存使用和 5 倍的拟合时间，同时提供 1500-2000 FPS 的更快渲染速度。此外，我们的编解码器在速度上也表现出色，达到了约 1000 FPS 的解码速度。