HybridNeRF: Efficient Neural Rendering

HybridNeRF is a novel method proposed for efficient neural rendering by combining the strengths of surface and volumetric representations. By rendering most objects as surfaces and modeling challenging regions volumetrically, HybridNeRF achieves improved error rates of 15-30% compared to state-of-the-art baselines. The approach enables real-time framerates of at least 36 FPS for virtual reality resolutions (2K×2K). Evaluation on the Eyeful Tower dataset and other commonly used datasets demonstrates the superior performance of HybridNeRF, outperforming previous methods like VR-NeRF in quality while rendering over 10 times faster. Additionally, comparisons with other datasets like ScanNet++ and Mip-NeRF 360 show that HybridNeRF excels in handling reflections, far-field content, and generating plausible surface geometry at faster speeds. The method's adaptability to different scenes and its ability to model semi-opaque and thin structures make it a promising advancement in neural rendering techniques.