NeRF

AI技术发展日新月异，其中一个近年来备受关注且极具颠覆性的概念，就是“神经辐射场”（Neural Radiance Fields），简称NeRF。这项技术犹如为数字世界打开了一扇“魔法之门”，让计算机能够以前所未有的真实感重建和渲染三维场景。

什么是NeRF？—— 让“照片活起来”的数字魔法

想象一下，你用手机对着一个物品或场景从不同角度拍摄了几张照片。传统上，这些照片只是平面的记忆。但NeRF却能通过这些看似普通的二维照片，像拥有魔力一般，“理解”这个三维场景的每一个细节、每一束光线，甚至预测你在任何一个从未拍摄过的角度看过去会是什么样子。它不是简单地把照片拼凑起来，而是真正地在计算机里“构建”了一个你可以自由探索的三维世界。

比喻一下：
如果说传统的3D建模就像是雕刻一个逼真的模型，需要精湛的技艺和大量的时间去刻画每一个面、每一条边；那么NeRF则更像是用几张照片作为“线索”，通过一个聪明的“画家”（神经网络）去“想象”并“重绘”出整个三维空间。这个“画家”不直接雕刻模型，而是学习了空间中每个点应该有什么颜色、透明度如何，最终能根据你的视角生成出逼真的画面。

NeRF如何实现这种“魔法”？

NeRF的核心在于利用神经网络来隐式地表示一个三维场景。这听起来有些抽象，我们来分解一下：

  1. 输入：多角度的照片和相机信息
    你提供给NeRF的，是同一个场景从不同位置、不同方向拍摄的多张二维照片，以及每张照片拍摄时相机所在的位置和朝向（就像知道你拍照时站在哪里、镜头对着哪个方向）。

  2. 核心“画家”：神经网络建模“辐射场”
    NeRF的关键是使用一个特殊的神经网络（通常是多层感知机，MLP）来模拟一个“神经辐射场”。这个“辐射场”不是一个实体模型，而更像是一本关于这个三维场景的“百科全书”。对空间中的任何一个点，以及任何一个观察方向，这本“百科全书”都能告诉你那里会发出什么颜色的光（颜色），以及有多少光会穿过去（透明度或密度）。

    • 像透明果冻盒子： 你可以把整个三维空间想象成一个巨大的透明果冻盒子，盒子里的每个细小到无法分辨的“果冻颗粒”都有自己的颜色和透明度。NeRF的神经网络就是学习如何描述这些“果冻颗粒”的性质。
    • 隐式表示： 这种表示方式被称为“隐式”表示，因为它并不直接建立传统的3D网格模型或点云，而是通过神经网络的数学函数来“记住”场景中的几何形状和光照信息。

  3. 学习与训练：从照片中“看懂”三维
    这个神经网络“画家”一开始是空白的，它需要通过学习来变得聪明。学习的过程就是对照你输入的照片：它会像人眼一样，从某个虚拟视角“看向”这个“透明果冻盒子”，根据里面“果冻颗粒”的颜色和透明度，计算出这条视线最终应该看到的颜色。然后，它将这个计算出的颜色与实际拍摄的照片进行比较，如果不同，就调整神经网络内部的参数，直到它能够准确地“复现”出所有输入照片看到的样子。通过反复的训练，神经网络就“掌握”了整个三维空间的颜色和透明度分布。

  4. 渲染与生成：创造前所未见的视角
    一旦神经网络训练完成，它就成了一个强大的“场景生成器”。你可以让它从任何一个全新的、从未拍摄过的角度去“看”这个场景，它都能根据学习到的“辐射场”信息，即时地渲染出一张逼真度极高的图像。

NeRF的优势何在？

  • 照片级真实感： NeRF生成的新视角图像具有极高的真实感和细节还原能力，让虚拟场景看起来几乎与真实照片无异。
  • 无需传统3D建模： 它摆脱了传统3D建模中繁琐的人工建模过程，只需多张二维照片即可重建三维场景。
  • 连续的场景表示： 神经网络提供的隐式表示是连续的，这意味着它能描述空间中任意精细的细节，不会因为离散化而丢失信息。

NeRF的应用场景

NeRF的出现为许多领域带来了新的可能性：

  • 虚拟现实（VR）和增强现实（AR）： 创建逼真的虚拟环境和数字内容，提高沉浸感。
  • 电影和游戏： 用于生成高质量的视觉效果、场景和动画，尤其是在电影制作中，可以实现更灵活的场景重现和视角切换。
  • 医学成像： 从2D扫描（如MRI）中重建出全面的解剖结构，为医生提供更有用的视觉信息。
  • 数字孪生与城市建模： 能够创建建筑物、城市乃至大型场景的详细数字复制品。
  • 机器人与自动驾驶： 帮助机器人和自动驾驶汽车更好地理解周围的三维环境。

NeRF的挑战与最新进展

尽管NeRF技术令人惊叹，但它仍面临一些挑战：

  • 计算资源和时间： 训练NeRF模型需要大量的计算资源和较长的时间。
  • 静态场景限制： 原始的NeRF主要适用于静态场景，对快速变化的动态场景处理能力有限。
  • 处理大规模场景的复杂性： 在处理超大范围的场景时，其效率和精度会受到影响。

为了克服这些局限，研究人员一直在不断改进NeRF技术。例如：

  • 效率优化： PixelNeRF、Mega-NeRD、NSVF等变体通过引入更有效的网络架构或稀疏表示，减少了所需的计算资源和训练时间，并提高了渲染速度。 “高斯飞溅”（Gaussian Splatting）等技术也在速度和质量上带来了显著改进，在某些方面超越了NeRF，但NeRF在内存效率和隐式表示的适应性方面仍有优势。
  • 动态场景和可编辑性： 一些新的研究方向正在探索如何让NeRF处理动态场景，以及如何直接编辑NeRF生成的场景内容，使其能像传统3D模型一样被修改。
  • 结合多模态数据： 未来的NeRF研究还可能结合文本、音频等其他输入，创造更丰富的交互与内容生成方式。
  • 应用拓展： 比如2024年的CVPR会议上，SAX-NeRF框架被提出，它能从稀疏的X光图像重建三维X光场景，无需CT数据。 清华大学的GenN2N框架则统一了多种NeRF到NeRF的转换任务，提升了编辑质量和效率。 基于NeRF的3D生成式AI也取得了突破，可以从单张图像生成可编辑的3D对象，或通过文本提示创造3D场景。

总而言之，NeRF及其衍生技术正在快速演进，它将二维照片转化为可交互三维场景的强大能力，无疑预示着未来数字内容创作和交互体验的巨大变革。 我们可以期待它在虚拟世界、媒体娱乐、医疗健康等诸多领域，带来无限可能。

NeRF

AI technology is evolving with each passing day. One of the concepts that has received much attention and is highly disruptive in recent years is “Neural Radiance Fields”, or NeRF for short. This technology is like opening a “magic door” for the digital world, allowing computers to reconstruct and render three-dimensional scenes with unprecedented realism.

What is NeRF? — Digital Magic That “Brings Photos to Life”

Imagine you take several photos of an object or scene from different angles with your mobile phone. Traditionally, these photos are just flat memories. But NeRF can use these seemingly ordinary two-dimensional photos, like having magic power, to “understand” every detail and every ray of light in this three-dimensional scene, and even predict what it would look like if you looked at it from any angle that has never been photographed. It doesn’t simply piece photos together, but truly “constructs” a three-dimensional world in the computer that you can explore freely.

Metaphor:
If traditional 3D modeling is like carving a realistic model, requiring superb skills and a lot of time to depict every face and every edge, then NeRF is more like using a few photos as “clues” and asking a clever “painter” (neural network) to “imagine” and “redraw” the entire three-dimensional space. This “painter” does not carve the model directly, but learns what color and transparency each point in space should have, and can finally generate a realistic picture according to your perspective.

How Does NeRF Achieve This “Magic”?

The core of NeRF lies in using neural networks to implicitly represent a three-dimensional scene. This sounds a bit abstract, let’s break it down:

  1. Input: Multi-angle Photos and Camera Information
    What you provide to NeRF are multiple two-dimensional photos of the same scene taken from different positions and directions, as well as the position and orientation of the camera when each photo was taken (like knowing where you stood and which direction the lens was facing when you took the photo).

  2. Core “Painter”: Neural Network Modeling “Radiance Field”
    The key to NeRF is using a special neural network (usually a Multi-Layer Perceptron, MLP) to simulate a “neural radiance field.” This “radiance field” is not a physical model, but more like an “encyclopedia” about this three-dimensional scene. For any point in space, and any observation direction, this “encyclopedia” can tell you what color of light will be emitted there (color), and how much light will pass through (transparency or density).

    • Like a Transparent Jelly Box: You can imagine the entire three-dimensional space as a huge transparent jelly box, where each “jelly particle” inside that is too small to distinguish has its own color and transparency. NeRF’s neural network learns how to describe the properties of these “jelly particles.”
    • Implicit Representation: This representation method is called “implicit” representation because it does not directly build traditional 3D mesh models or point clouds, but “remembers” the geometric shape and lighting information in the scene through the mathematical functions of the neural network.

  3. Learning and Training: “Reading” 3D from Photos
    This neural network “painter” is blank at the beginning, and it needs to become smart through learning. The process of learning is to compare against the photos you input: it will “look at” this “transparent jelly box” from a virtual perspective like a human eye. Based on the color and transparency of the “jelly particles” inside, it calculates the color that this line of sight should see. Then, it compares this calculated color with the actual photo taken. If different, it adjusts the internal parameters of the neural network until it can accurately “reproduce” the appearance seen in all input photos. Through repeated training, the neural network “masters” the color and transparency distribution of the entire three-dimensional space.

  4. Rendering and Generation: Creating Unseen Perspectives
    Once the neural network training is completed, it becomes a powerful “scene generator.” You can let it “look” at this scene from any new angle that has never been photographed, and it can instantly render a highly realistic image based on the learned “radiance field” information.

What are the Advantages of NeRF?

  • Photo-realistic Quality: New perspective images generated by NeRF have extremely high realism and detail restoration capabilities, making virtual scenes look almost identical to real photos.
  • No Need for Traditional 3D Modeling: It gets rid of the tedious manual modeling process in traditional 3D modeling, and can reconstruct three-dimensional scenes with just multiple two-dimensional photos.
  • Continuous Scene Representation: The implicit representation provided by the neural network is continuous, which means it can describe arbitrarily fine details in space without losing information due to discretization.

Application Scenarios of NeRF

The emergence of NeRF has brought new possibilities to many fields:

  • Virtual Reality (VR) and Augmented Reality (AR): Creating realistic virtual environments and digital content, improving immersion.
  • Movies and Games: Used to generate high-quality visual effects, scenes, and animations. Especially in film production, it can achieve more flexible scene reproduction and perspective switching.
  • Medical Imaging: Reconstructing comprehensive anatomical structures from 2D scans (such as MRI), providing doctors with more useful visual information.
  • Digital Twins and City Modeling: Capable of creating detailed digital replicas of buildings, cities, and even large scenes.
  • Robotics and Autonomous Driving: Helping robots and autonomous vehicles better understand the surrounding three-dimensional environment.

Challenges and Latest Developments in NeRF

Although NeRF technology is amazing, it still faces some challenges:

  • Computing Resources and Time: Training NeRF models requires a lot of computing resources and a long time.
  • Static Scene Limitations: The original NeRF is mainly suitable for static scenes and has limited processing capabilities for rapidly changing dynamic scenes.
  • Complexity of Processing Large-scale Scenes: Efficiency and accuracy will be affected when processing ultra-large-scale scenes.

To overcome these limitations, researchers have been continuously improving NeRF technology. For example:

  • Efficiency Optimization: Variants like PixelNeRF, Mega-NeRD, and NSVF reduce required computing resources and training time and improve rendering speed by introducing more effective network architectures or sparse representations. Technologies such as “Gaussian Splatting” have also brought significant improvements in speed and quality, surpassing NeRF in some aspects, but NeRF still has advantages in memory efficiency and adaptability of implicit representation.
  • Dynamic Scenes and Editability: Some new research directions are exploring how to let NeRF handle dynamic scenes, and how to directly edit the scene content generated by NeRF so that it can be modified like traditional 3D models.
  • Combining Multi-modal Data: Future NeRF research may also combine other inputs such as text and audio to create richer interaction and content generation methods.
  • Application Expansion: For example, at the CVPR conference in 2024, the SAX-NeRF framework was proposed, which can reconstruct three-dimensional X-ray scenes from sparse X-ray images without CT data. Tsinghua University’s GenN2N framework unified multiple NeRF-to-NeRF conversion tasks, improving editing quality and efficiency. 3D generative AI based on NeRF has also made breakthroughs, generating editable 3D objects from a single image, or creating 3D scenes through text prompts.

In summary, NeRF and its derivative technologies are evolving rapidly. Its powerful ability to convert two-dimensional photos into interactive three-dimensional scenes undoubtedly heralds a huge revolution in future digital content creation and interactive experiences. We can look forward to it bringing infinite possibilities in many fields such as the virtual world, media entertainment, medical health, etc.