HeRF: A Hierarchical Framework for Efficient and Extendable New View Synthesis

Recently, neural radiance fields have made significant advancements in rendering new views. However, limited research has focused on dynamically loading implicit radiance fields with efficient memory utilization and extended scene representation. This paper introduces HeRF, a novel framework with a hierarchical scene representation based on layered sparse voxels. With such an adaptive design, our method is able to partition scenes into different levels for faster modeling and reduced memory cost. Furthermore, these partitioned scenes can be dynamically loaded and joined for a better immersive experience. Quantitative and qualitative analysis using objectlevel, indoor, and outdoor datasets demonstrates the effectiveness of HeRF. Remarkably, our proposed method requires only about 38% of the training rays and 45% of the GPU memory cost, yet achieves a 9% improvement in PSNR compared to NeRFusion on the ScanNet dataset. The code is available at https://github.com/Minisal/HeRF.