Neural Rendering and Its Hardware Acceleration: A Review

TL;DR

This review paper discusses neural rendering, its integration with deep learning and physical knowledge, hardware acceleration challenges, and future trends in processor architecture to support applications like VR, AI, and digital entertainment.

Contribution

It provides a comprehensive overview of neural rendering techniques, analyzes hardware acceleration requirements, and discusses design challenges and future directions for neural rendering processors.

Findings

Neural rendering combines deep learning with physical scene models.

Hardware acceleration is crucial for real-time neural rendering applications.

Future neural rendering processors need to address design challenges for efficiency and scalability.

Abstract

Neural rendering is a new image and video generation method based on deep learning. It combines the deep learning model with the physical knowledge of computer graphics, to obtain a controllable and realistic scene model, and realize the control of scene attributes such as lighting, camera parameters, posture and so on. On the one hand, neural rendering can not only make full use of the advantages of deep learning to accelerate the traditional forward rendering process, but also provide new solutions for specific tasks such as inverse rendering and 3D reconstruction. On the other hand, the design of innovative hardware structures that adapt to the neural rendering pipeline breaks through the parallel computing and power consumption bottleneck of existing graphics processors, which is expected to provide important support for future key areas such as virtual and augmented reality, film and television creation and digital entertainment, artificial intelligence and the metaverse. In this paper, we review the technical connotation, main challenges, and research progress of neural rendering. On this basis, we analyze the common requirements of neural rendering pipeline for hardware acceleration and the characteristics of the current hardware acceleration architecture, and then discuss the design challenges of neural rendering processor architecture. Finally, the future development trend of neural rendering processor architecture is prospected.