Synthesizing photo-realistic images and videos is at the heart of computer graphics and has been the focus of decades of research. Traditionally, synthetic images of a scene are generated using rendering algorithms such as rasterization or ray tracing, which take specifically defined representations of geometry and material properties as input. Collectively, these inputs define the actual scene and what is rendered, and are referred to as the scene representation (where a scene consists of one or more objects). Example scene representations are triangle meshes with accompanied textures (e.g., created by an artist), point clouds (e.g., from a depth sensor), volumetric grids (e.g., from a CT scan), or implicit surface functions (e.g., truncated signed distance fields). The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering. Neural rendering is closely related, and combines ideas from classical computer graphics and machine learning to create algorithms for synthesizing images from real-world observations. Neural rendering is a leap forward towards the goal of synthesizing photo-realistic image and video content. In recent years, we have seen immense progress in this field through hundreds of publications that show different ways to inject learnable components into the rendering pipeline. This state-of-the-art report on advances in neural rendering focuses on methods that combine classical rendering principles with learned 3D scene representations, often now referred to as neural scene representations. A key advantage of these methods is that they are 3D-consistent by design, enabling applications such as novel viewpoint synthesis of a captured scene. In addition to methods that handle static scenes, we cover neural scene representations for modeling non-rigidly deforming objects...
翻译:合成摄影现实图像和视频是计算机图形的核心,是数十年研究的重点。传统上,一个场景的合成图像是利用演算法生成的,如光学或射线追踪,这些算法以具体定义的几何和材料属性表示为输入。这些投入定义了实际场景和所拍摄的图像,并被称为场景表示法(场景由一个或多个对象组成 )。一些场景表示法是三角形的介质,配有伴随的纹理(例如,由一位艺术家创建的静态)、点云(例如,来自深度传感器)、体积电网(例如,来自CT扫描)或隐含的表面功能(例如,通过具体限定的距离场景表示法,通过不同模型显示的场景表达法或反面表达法,这些模型和机器的演化法结合了从真实世界观测的图像的同步化图象(例如,从深度传感器扫描或射线网图象追踪)的计算法,现在显示的是向前方的直径直径向前方展示方法,通过图像显示我们所了解的场面图象的图像显示的直径直径直径直径直径。 在图像中,这些图像显示中,这些直径图解的图象显示中,从直径直径直径直到直到直到直径直径图解的图解的图解的图解的图解为直向直向直向直向直向直向直向直径向直向,通过图像面显示为直向,通过直向,从直径径图,从直向直向向向向向向,从直向直向直向中,从直向向向向向中,通过直到直向中,从直向向直向直向中,通过直向直到直向直向向的直向中,通过直向直向直向中,通过直到直向中,通过直向向向直向直向直向中,直向直向直向直向中,通过直向直向直向直向直向直向直向中,通过直向直向直向中,从直向直向右,通过直向右向直向直向直向右向右,通过直向直向右向右,通过直向直向直向直向直向