神经辐射场(NeRF)是一种用于重建三维场景的新兴方法,其在机器人领域的应用前景备受关注。NeRF通过多层感知机(MLP)学习三维场景特征,实现高保真的图像渲染,并为机器人在复杂环境中的导航、定位和感知提供基础。其核心流程包括光线采样、特征提取和体渲染,具有计算量大和非规则存储访问密集的特点,限制了在现有硬件平台,尤其是端侧设备上的部署,亟需探索新的硬件架构和软硬件协同优化方案。本文系统阐述了NeRF的技术原理与算法演进,并探讨了其在现有硬件设备上的性能瓶颈。在此基础上,详细介绍了经典的NeRF硬件加速器工作,归纳出图像相似性优化、空间稀疏性优化、存储器访问优化三种主要优化方向,并分析了不同工作技术的共性与差异。此外,结合SLAM、AIGC等应用场景,探讨了当前NeRF加速器在处理开放场景任务时所面临的可扩展性和存储限制方面的技术局限和挑战。最后,提出未来发展的建议,以期为NeRF硬件加速器的进一步应用和优化提供启发。

Neural Radiance Fields (NeRF) is an emerging method for reconstructing 3D scenes, garnering significant attention for its potential applications in the field of robotics. NeRF uses Multi-Layer Perceptrons (MLPs) to learn 3D scene features, achieving high-fidelity image rendering and providing a foundation for navigation, localization, and perception in complex environments. Its core processes, including ray sampling, feature extraction, and volumetric rendering, are computationally intensive and involve irregular memory access patterns, which limits deployment on existing hardware platforms, especially edge devices. To advance the practical application of NeRF technology, new hardware architectures and solutions for co-optimization of hardware and software are necessary. This review systematically elucidates the principles and evolution of NeRF technology, exploring the performance bottlenecks encountered during its hardware execution. The review provides a detailed review of classic NeRF hardware accelerators, summarizing three main optimization directions: image similarity optimization, spatial sparsity optimization, and memory access optimization, and analyzes the commonalities and differences among various techniques. Additionally, the review examines the technical limitations and challenges of current NeRF accelerators in handling open scene tasks, considering applications such as SLAM and AIGC, particularly in terms of scalability and storage constraints. Finally, the review offers suggestions for future development to inspire further applications and optimization of NeRF hardware accelerators.