Application and optimization of neural radiation field (NeRF) in 3D scene reconstruction

Background This experiment aims to optimize NeRF's 3D reconstruction scene algorithm and evaluate the performance of the optimized neural radiation field (NeRF) model in 3D scene reconstruction. Methods: This study proposes an optimized NeRF model that integrates multi-scale feature extraction, hybrid voxel representation and an early ray termination strategy. The model is validated by comparing its reconstruction accuracy and computational efficiency with those of traditional NeRF, Mip-NeRF and PlenOctrees in both static and cross-scene settings. Results The experimental findings demonstrate the optimized NeRF model's notable benefits in a number of areas, especially in training time, inference time, and memory usage. In static object scenes, the optimized NeRF model reduces training time by about 50%, inference time by 42%, and memory usage by 40% while maintaining reconstruction accuracy (SSIM and PSNR) similar to traditional NeRF models. In cross-scene settings, although the reconstruction accuracy slightly decreases, the optimized NeRF model still outperforms traditional NeRF in SSIM and PSNR metrics, and reduces training time and inference time by 51% and 47%, respectively, while reducing memory usage by over 40%. Compared with the Mip NeRF and PlenOctrees models, the optimized NeRF model has stronger competitiveness in inference time and memory usage, especially in cross-scene scenarios. Conclusion Overall, the optimized NeRF model not only improves computational efficiency, but also maintains high reconstruction accuracy in different scenarios, demonstrating its practical application potential on large-scale datasets and resource constrained devices. The experiment shows that the optimization strategy effectively improves the comprehensive performance of NeRF models in 3D reconstruction tasks, especially in terms of processing efficiency and storage space utilization.