Advances in Neural Rendering (2111.05849v2)

Abstract: 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...

A Comprehensive Overview of Recent Advances in Neural Rendering

The paper "Advances in Neural Rendering," accepted at EUROGRAPHICS 2022, presents a detailed and extensive review of state-of-the-art methodologies and the evolution of neural rendering techniques. Neural rendering stands at the intersection of computer graphics and machine learning, aiming to synthesize photo-realistic images and videos from real-world observations by integrating learnable components into the traditional rendering pipeline. The authors, a collective of researchers from esteemed institutions and organizations, delineate the substantial progress made in the field, showcasing various approaches to augment traditional rendering principles with neural networks.

Key Contributions and Methodologies

The paper emphasizes several major contributions to the landscape of neural rendering:

1. Neural Scene Representations: The paper details the adoption and advancement of neural scene representations, which utilize multi-layer perceptrons (MLPs) as universal function approximators. These representations offer a compact and continuous means to parameterize scenes, enabling efficient synthesis of high-resolution images. The introduction of positional encoding, as seen in Neural Radiance Fields (NeRF), has significantly enhanced the fidelity of these representations by allowing them to model high-frequency components effectively.
2. Efficient Rendering Techniques: By leveraging volumetric rendering, neural rendering methods achieve notable improvements in rendering speed and image quality. Techniques such as Neural Sparse Voxel Fields and KiloNeRF employ data structures like octrees and voxel grids to optimize rendering by skipping empty space and focusing computation on relevant areas, thereby reducing both time and computational expense.
3. Applications and Generalization: The paper discusses the broad range of applications for neural rendering, from novel view synthesis and dynamic scene modeling to relighting and scene editing. It also underscores the advances in methods that generalize across scenes and objects, enabling efficient rendering from sparse data and fostering enhanced 3D consistency in synthesized views.
4. Challenges and Future Directions: Despite the significant strides made, the authors acknowledge several areas that present challenges, including scalability, generalizability, and the integration of neural rendering into traditional computer graphics workflows. Addressing these challenges is pivotal for further advancements and the seamless application of neural rendering in diverse domains.

Numerical Results and Claims

While this paper mainly provides a state-of-the-art report, it highlights impactful results from various neural rendering techniques. These advancements reflect the refinement in rendering quality, computational efficiency, and applicability to real-world scenarios. They collectively demonstrate the feasibility of neural rendering as a robust approach to photo-realistic image and video synthesis, capable of competing with, and in some cases surpassing, traditional rendering pipelines.

Implications and Speculations

Theoretical implications of this research extend into the realms of computer vision and machine learning, where neural rendering techniques can provide new capabilities in scene understanding and simulation. Practically, neural rendering holds significant potential for revolutionizing industries reliant on graphics, such as virtual reality, film, and game development, by drastically reducing the time and resources required for high-quality content creation.

The paper speculates that future developments may focus on enhancing the scalability and efficiency of neural representations and integrating novel data modalities to further enrich the rendering process. Incorporating elements such as semantics, audio, and other sensory information will likely broaden the horizons of neural rendering applications.

Conclusion

The report by Tewari et al. offers a comprehensive view of the exciting developments in neural rendering, providing insights into state-of-the-art methods and their applications. As the field advances, overcoming the discussed challenges will be crucial for unleashing its full potential, paving the way for neural rendering to become a cornerstone technology in graphics and beyond.