摘要
Abstract
Neural Radiance Fields(NeRF)is an emerging method for reconstructing 3D scenes,garnering significant attention for its po-tential 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,in-cluding 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 systemat-ically 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 direc-tions: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 accel-erators 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 hard-ware accelerators.关键词
神经辐射场/3D重建/图像渲染/加速器/TensoRFKey words
NeRF/3D reconstruction/graphic rendering/accelerator/TensoRF分类
信息技术与安全科学
