ASFC-NeRF: Large-Scale Scene Rendering with Adaptive Sampling and Feature-aware Compression

While significant progress has been made in large-scale scene representation using Neural Radiance Fields (NeRF), several limitations remain. For instance, most methods still rely on the original coarse-to-fine sampling strategy, leading to an inefficient rendering process. Additionally, to model larger scenes, these methods often use complex network models, resulting in redundant model parameters. To address these issues, we propose a novel model with adaptive sampling and feature-aware compression for large-scale scene rendering, named ASFC-NeRF. We first introduce a weight prediction network to replace the original coarse sampling strategy, then employ a teacher network and depth constraints for knowledge distillation in the early stages of training to enhance the high-fidelity of the scene. Furthermore, we optimize the number of Grids and the channels of Planes and prune the network to efficiently compress model parameters. Experimental results demonstrate that our method significantly accelerates the rendering process and greatly reduces parameter quantity while maintaining or only slightly lowering image quality. Therefore, ASFC-NeRF exhibits advantages in comprehensive performance and practicality.