Gaussian Splatting

Gaussian Splatting： 用无数“发光点”重塑三维世界

在数字时代，我们已经习惯了在屏幕上欣赏栩栩如生的2D照片和视频。但如何才能让这些平面的图像“活”过来，变成可以在三维空间中自由探索的场景呢？想象一下，你可以在电脑上像身临其境一样，从任何角度观察一个房间，甚至可以走进去，感受它的空间细节。这就是我们今天要聊的主角——Gaussian Splatting (高斯泼溅)，一项在AI三维重建领域掀起风暴的创新技术。

一、告别“像素格”，迎接“模糊小球”

我们日常看到的2D图片，无论多么高清，本质上都是由一个个微小的、方形的“像素格”组成的。每个像素有自己的颜色，排列起来就构成了画面。如果类比到三维世界，传统的3D模型通常是由成千上万个微小的三角形面片（多边形）拼接而成，就像用很多小纸片折出一个复杂的形状。

而Gaussian Splatting则提出了一种全新的“积木”方式。它不再使用固定的像素或三角形，而是将三维空间中的物体和场景，分解成无数个**“三维高斯球”**，或者你可以想象成一个个拥有颜色、大小、形状和透明度的“模糊小球”或者“彩色的云团”。 3D Gaussian Splatting是一种基于高斯函数的场景重建和渲染技术，它将场景中的点云数据转化为高斯函数的形式，从而实现对场景的建模和渲染。 具体来说，每个点都被表示为一个具有均值和协方差的高斯函数，这个函数描述了该点周围的颜色和亮度分布，通过对这些高斯函数进行叠加和渲染，可以生成高质量的3D场景图像。

类比一下： 如果说传统3D模型是用严谨的砖块搭建房屋，那么Gaussian Splatting更像是用喷笔在空中喷洒出无数彩色的、半透明的雾气团，这些雾气团在空间中飘散、重叠，最终在我们观察者的眼中汇聚成一个立体而真实的景象。

二、高斯球的秘密：不只是一个点

那么，这些“模糊小球”到底有什么特别之处呢？每一个高斯球，都不仅仅是一个简单的点。它包含了以下几个关键信息，让它能精准地“描绘”出三维世界的各个细节：

  1. 空间位置 (XYZ坐标)： 它在三维空间中的具体位置，就好比这个雾气团“飘”在哪里。
  2. 大小与形状 (尺度与协方差)： 它可以是圆滚滚的球形，也可以被拉伸成椭球形，甚至可以像一片被压扁的叶子。这决定了它“覆盖”的空间范围和形状。协方差矩阵则定义了高斯分布的形状和方向，如同一个可以调整形状和角度的模具。
  3. 旋转 (四元数)： 这个椭球形的雾气团在空间中是如何摆放的，是竖着、横着还是斜着。
  4. 颜色 (RGB值或球谐函数系数)： 它的具体色调，是红色、蓝色还是绿色，甚至可以根据观察角度呈现不同的颜色。
  5. 透明度 (Alpha值)： 它的透明程度，是完全不透明，还是像薄纱一样若隐若现。

类比一下： 想象你在一个黑暗的房间里，手里拿着无数个可以调节大小、形状、颜色和亮度的迷你手电筒。你把这些手电筒扔到房间的各个角落，有些是圆的，有些是扁的，有些是亮的，有些是暗的。当它们各自发出光线，并通过重叠来描绘出房间中的所有物体时，你所看到的就是由这些“光团”组合而成的立体景象。

三、如何“泼溅”出真实世界？

Gaussian Splatting之所以叫“泼溅”，是因为它渲染图像的方式，非常形象地诠释了这个过程。当我们需要从某个角度观察这个三维场景时，系统会把所有可见的、符合条件的高斯球**“泼溅”**到我们眼前的二维图像平面上，这个过程被形象地称为”Splatting”。

具体来说，它会计算每个高斯球如何从当前视角下投影到屏幕上，并根据其透明度、颜色和深度顺序进行巧妙地融合。背后有一套高效的排序和混合算法（如Alpha混合），确保离我们近的物体能正确地遮挡住远处的物体，同时半透明的物体又能透出后面的景象。

类比一下： 这就像一个经验丰富的画家，不是先画出物体的轮廓再填充颜色，而是直接在画布上泼洒彩色的颜料团。他知道哪个颜料团应该放在哪里，哪个应该透明一些，哪个应该覆盖住另一个，最终这些颜料团层层叠叠，就形成了我们看到的逼真画作。

四、AI如何学会“泼溅”？

Gaussian Splatting之所以能够如此神奇，核心在于其强大的学习能力。它不需要你亲自去手工摆放、调整这些高斯球，而是通过机器学习自动完成。

  1. 输入影像： 你只需要用普通相机围绕一个物体或场景拍摄一系列不同角度的照片或视频。
  2. AI学习： AI系统（通常基于复杂的优化算法）会分析这些2D图像，并尝试在三维空间中“猜测”出最初的几百或几千个高斯球。这个过程通常从稀疏的点云数据（通过运动恢复结构SfM生成）开始初始化3D高斯数集。
  3. 迭代优化： 接下来，AI会不断地调整这些高斯球的位置、大小、形状、颜色和透明度。它会生成当前的高斯球渲染出来的图像，然后将这个渲染图与原始的输入图像进行比较。通过L1和D-SSIM损失函数进行随机梯度下降，优化高斯球的各项参数。如果存在差异，AI就会“知道”自己的高斯球参数不够准确，需要进一步调整。
  4. 自适应增加与修剪： 在需要更多细节的区域，AI会自动“分裂”出更多的高斯球，使得局部表现更加精细；而在细节较少的区域，则会删除或合并不必要的高斯球，以优化模型和训练效率。

这个过程就像一个学徒画家，拿着老师提供的参考照片（输入图像），在空白画布上不停地调整自己的画笔（高斯球参数），直到他画出的画与参考照片一模一样，甚至能够从参考照片中没有的角度，也画出逼真的景象。

五、为什么Gaussian Splatting如此引人注目？

在Gaussian Splatting之前，NeRF（Neural Radiance Fields，神经辐射场）是三维重建领域的热门技术。虽然NeRF也能实现惊人的效果，但Gaussian Splatting带来了显著的改进：

  1. 渲染速度飞快： 这是其最大的优势之一。NeRF渲染一张高质量图像可能需要数秒甚至更长时间，而Gaussian Splatting可以达到实时渲染（每秒30帧以上，甚至可达90帧），这意味着你可以非常流畅地在场景中穿梭。 这种速度优势使其在VR/AR应用中具有巨大潜力。
  2. 训练速度更快： 从原始图片到生成可用的三维场景，Gaussian Splatting的训练时间也大大缩短，从数小时缩短到几分钟甚至几十秒，同时保持有竞争力的训练时间。
  3. 重建质量更高： 通常能捕捉到更精细的纹理和几何细节，生成更清晰、更真实的图像。它在保持最先进的视觉质量的同时，避免了空白空间不必要的计算。
  4. 可编辑性增强： 尽管仍是研究热点，但高斯球的显式离散特性使得对场景进行编辑（如动态重建、几何编辑和物理模拟）变得更为容易。

类比一下： 如果说NeRF是一个需要超级计算机才能在后台慢慢绘制的精美油画，那么Gaussian Splatting则像是一个掌握了速写技巧的大师，可以用更快的速度、更少的笔触，直接在你的眼前绘制出一幅同样精美，甚至细节更丰富的作品，并且你还可以要求他快速地在画中修改一个细节。

六、应用前景与最新进展

Gaussian Splatting一经问世，便迅速捕获了学术界和工业界的目光。其高速、高质的特性，为其在诸多领域打开了广阔的应用前景：

  • VR/AR领域： 提供高度逼真的虚拟环境和沉浸式体验，用户可以在重建的真实世界场景中自由探索，无需等待漫长的加载时间。 在AR导航中，可以将虚拟指示以逼真效果叠加在真实街道上。
  • 数字孪生与遗产保护： 快速、精确地创建现实世界的数字副本，用于城市规划、文物修复和文化遗产的数字化展示。
  • 机器人技术与自动驾驶： 3D Gaussian Splatting可以用于构建精确的3D环境模型，帮助机器人实现导航、建图和感知等功能，在自动驾驶领域可用于构建高清地图和感知周围环境。
  • 电影与游戏制作： 极大简化3D模型的创建流程，降低成本，提高效率，尤其是在需要从现实场景生成数字资产时。
  • 电子商务与产品展示： 消费者可以在线以任意角度“触摸”和观察商品细节，提升线上购物体验。
  • 人体建模和动画： 用于生成逼真的虚拟人物和动画效果。
  • 同步定位与建图 (SLAM)： 3D Gaussian Splatting正在重塑SLAM技术，提供了对场景的高效、高质量渲染，有助于系统在未知环境中定位自身并构建地图。

最新的研究进展使得Gaussian Splatting的应用场景进一步扩展。例如，研究人员正在探索如何实现动态场景的重建，即能够捕捉并渲染出运动中的物体或人物，例如通过建模高斯属性值随时间的变化，或者将3D高斯转换为4D高斯进行切片渲染。 此外，最新的”DepthSplat”模型将Gaussian Splatting与多视图深度估计结合，提升了深度估计和新视角合成的性能。 还有研究致力于将Gaussian Splatting与大型语言模型（LLM）结合，实现通过自然语言描述来编辑3D场景，使其智能化程度更高。

总结来说， Gaussian Splatting就像是在三维重建领域引入了一种全新的“魔术”，它用无数个可以精细调整的“彩色模糊小球”，不仅重构了现实世界，更提升了我们与数字世界互动的方式和效率。它让我们离“复制”和“体验”真实世界又近了一步，而这仅仅是个开始。

Gaussian Splatting: Remaking the 3D World with Countless “Glowing Points”

In the digital age, we have become accustomed to enjoying lifelike 2D photos and videos on screens. But how can we make these flat images “come alive” and become scenes that can be freely explored in three-dimensional space? Imagine being able to observe a room from any angle on your computer as if you were there, or even walk in and feel its spatial details. This is the protagonist we are talking about today — Gaussian Splatting, an innovative technology that has taken the field of AI 3D reconstruction by storm.

1. Farewell to “Pixels”, Welcome “Fuzzy Little Balls”

The 2D pictures we see every day, no matter how high-definition, are essentially composed of tiny, square “pixels”. Each pixel has its own color, and together they form the picture. By analogy to the 3D world, traditional 3D models are usually stitched together from thousands of tiny triangular patches (polygons), just like folding a complex shape with many small pieces of paper.

Gaussian Splatting proposes a brand-new way of “building blocks”. It no longer uses fixed pixels or triangles but decomposes objects and scenes in 3D space into countless “3D Gaussian Spheres”, or you can imagine them as “fuzzy little balls“ or “colored cloud clusters“ with color, size, shape, and transparency. 3D Gaussian Splatting is a scene reconstruction and rendering technology based on Gaussian functions. It converts point cloud data in the scene into the form of Gaussian functions to achieve scene modeling and rendering. Specifically, each point is represented as a Gaussian function with mean and covariance. This function describes the color and brightness distribution around the point. By superimposing and rendering these Gaussian functions, high-quality 3D scene images can be generated.

Analogy: If traditional 3D models are like building houses with rigorous bricks, then Gaussian Splatting is more like spraying countless colored, translucent mist clusters in the air with an airbrush. These mist clusters float and overlap in space, and finally converge into a three-dimensional and realistic scene in the eyes of our observers.

2. The Secret of Gaussian Spheres: Not Just a Point

So, what is so special about these “fuzzy little balls”? Each Gaussian sphere is not just a simple point. It contains the following key information, allowing it to accurately “depict” every detail of the 3D world:

  1. Spatial Position (XYZ Coordinates): Its specific position in 3D space, just like where this mist cluster “floats”.
  2. Size and Shape (Scale and Covariance): It can be a round sphere, stretched into an ellipsoid, or even like a flattened leaf. This determines the spatial range and shape it “covers”. The covariance matrix defines the shape and direction of the Gaussian distribution, like a mold whose shape and angle can be adjusted.
  3. Rotation (Quaternion): How this ellipsoidal mist cluster is placed in space: vertically, horizontally, or obliquely.
  4. Color (RGB Values or Spherical Harmonics Coefficients): Its specific hue, whether red, blue, or green, can even present different colors depending on the viewing angle.
  5. Transparency (Alpha Value): Its degree of transparency, whether completely opaque or looming like a veil.

Analogy: Imagine you are in a dark room holding countless mini flashlights with adjustable size, shape, color, and brightness. You throw these flashlights into every corner of the room; some are round, some are flat, some are bright, and some are dim. When they each emit light and depict all the objects in the room through overlapping, what you see is a three-dimensional scene composed of these “light clusters”.

3. How to “Splat” a Real World?

The reason why Gaussian Splatting is called “Splatting” is that the way it renders images vividly interprets this process. When we need to observe this 3D scene from a certain angle, the system will “splat” all visible and eligible Gaussian spheres onto the 2D image plane in front of us. This process is vividly called “Splatting”.

Specifically, it calculates how each Gaussian sphere is projected onto the screen from the current perspective and cleverly blends them according to their transparency, color, and depth order. Behind this is a set of efficient sorting and blending algorithms (such as Alpha Blending) ensuring that objects close to us correctly occlude distant objects, while semi-transparent objects reveal the scene behind them.

Analogy: It’s like an experienced painter who doesn’t draw the outline of an object first and then fill in the color, but directly splashes colored pigment clusters on the canvas. He knows which pigment cluster should be placed where, which should be transparent, and which should cover another. In the end, these pigment clusters overlap layer by layer to form the realistic painting we see.

4. How Does AI Learn to “Splat”?

The core reason why Gaussian Splatting can be so magical lies in its powerful learning ability. It doesn’t require you to manually place and adjust these Gaussian spheres but completes it automatically through Machine Learning.

  1. Input Images: You only need to specific a series of photos or videos of an object or scene from different angles with an ordinary camera.
  2. AI Learning: The AI system (usually based on complex optimization algorithms) analyzes these 2D images and tries to “guess” the initial hundreds or thousands of Gaussian spheres in 3D space. This process usually starts from sparse point cloud data (generated by Structure from Motion, SfM) to initialize the 3D Gaussian set.
  3. Iterative Optimization: Next, the AI constantly adjusts the position, size, shape, color, and transparency of these Gaussian spheres. It generates an image rendered by the current Gaussian spheres and compares this rendered image with the original input image. Through Stochastic Gradient Descent with L1 and D-SSIM loss functions, the parameters of the Gaussian spheres are optimized. If there is a difference, the AI “knows” that its Gaussian sphere parameters are not accurate enough and need further adjustment.
  4. Adaptive Density Control: In areas requiring more detail, AI automatically “splits” into more Gaussian spheres to make local performance finer; while in areas with fewer details, it deletes or merges unnecessary Gaussian spheres to optimize the model and training efficiency.

This process is like an apprentice painter holding a reference photo provided by the teacher (input image) and constantly adjusting his brush (Gaussian sphere parameters) on a blank canvas until the painting he draws is exactly the same as the reference photo, and can even draw realistic scenes from angles not present in the reference photo.

5. Why is Gaussian Splatting So Compelling?

Before Gaussian Splatting, NeRF (Neural Radiance Fields) was a popular technology in the field of 3D reconstruction. Although NeRF can also achieve amazing results, Gaussian Splatting brings significant improvements:

  1. Fast Rendering Speed: This is one of its biggest advantages. Rendering a high-quality image with NeRF may take seconds or even longer, while Gaussian Splatting can achieve real-time rendering (over 30 frames per second, even up to 90 frames), which means you can shuttle through the scene very smoothly. This speed advantage gives it huge potential in VR/AR applications.
  2. Faster Training Speed: The training time from raw images to generating usable 3D scenes is also greatly shortened for Gaussian Splatting, from hours to minutes or even tens of seconds, while maintaining competitive training times.
  3. Higher Reconstruction Quality: It usually captures finer textures and geometric details, generating clearer and more realistic images. It maintains state-of-the-art visual quality while avoiding unnecessary calculations in empty spaces.
  4. Enhanced Editability: Although still a research hotspot, the explicit discrete nature of Gaussian spheres makes scene editing (such as dynamic reconstruction, geometric editing, and physical simulation) easier.

Analogy: If NeRF is an exquisite oil painting that requires a supercomputer to slowly draw in the background, then Gaussian Splatting is like a master who has mastered sketching skills. He can draw a work that is equally exquisite and even richer in detail directly in front of your eyes with faster speed and fewer strokes, and you can also ask him to quickly modify a detail in the painting.

6. Application Prospects and Latest Progress

Once Gaussian Splatting was introduced, it quickly captured the attention of academia and industry. Its high-speed and high-quality characteristics have opened up broad application prospects in many fields:

  • VR/AR Field: Providing highly realistic virtual environments and immersive experiences, users can freely explore reconstructed real-world scenes without waiting for long loading times. In AR navigation, virtual instructions can be superimposed on real streets with realistic effects.
  • Digital Twins and Heritage Preservation: Quickly and accurately creating digital copies of the real world for urban planning, cultural relic restoration, and digital display of cultural heritage.
  • Robotics and Autonomous Driving: 3D Gaussian Splatting can be used to build precise 3D environment models to help robots achieve navigation, mapping, and perception functions. In the field of autonomous driving, it can be used to build high-definition maps and perceive the surrounding environment.
  • Film and Game Production: Greatly simplifying the creation process of 3D models, reducing costs, and improving efficiency, especially when generating digital assets from real scenes.
  • E-commerce and Product Display: Consumers can “touch” and observe product details online from any angle, improving the online shopping experience.
  • Human Body Modeling and Animation: Used to generate realistic virtual characters and animation effects.
  • Simultaneous Localization and Mapping (SLAM): 3D Gaussian Splatting is reshaping SLAM technology, providing efficient and high-quality rendering of scenes, helping systems position themselves and build maps in unknown environments.

The latest research progress has further expanded the application scenarios of Gaussian Splatting. For example, researchers are exploring how to achieve reconstruction of dynamic scenes, capable of capturing and rendering moving objects or people, such as by modeling the changes in Gaussian property values over time, or converting 3D Gaussians to 4D Gaussians for slice rendering. In addition, the latest “DepthSplat“ model combines Gaussian Splatting with multi-view depth estimation, improving the performance of depth estimation and novel view synthesis. There is also research dedicated to combining Gaussian Splatting with Large Language Models (LLMs) to achieve editing of 3D scenes through natural language descriptions, making them more intelligent.

In summary, Gaussian Splatting is like introducing a new kind of “magic” into the field of 3D reconstruction. With countless “colored fuzzy little balls” that can be finely adjusted, it not only reconstructs the real world but also improves the way and efficiency of our interaction with the digital world. It brings us one step closer to “copying” and “experiencing” the real world, and this is just the beginning.