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Neural radiance fields (NeRF) bring a new wave for 3D interactiveexperiences. However, as an important part of the immersive experiences, thedefocus effects have not been fully explored within NeRF. Some recentNeRF-based methods generate 3D defocus effects in a post-process fashion byutilizing multiplane technology. Still, they are either time-consuming ormemory-consuming. This paper proposes a novel thin-lens-imaging-based NeRFframework that can directly render various 3D defocus effects, dubbed NeRFocus.Unlike the pinhole, the thin lens refracts rays of a scene point, so itsimaging on the sensor plane is scattered as a circle of confusion (CoC). Adirect solution sampling enough rays to approximate this process iscomputationally expensive. Instead, we propose to inverse the thin lens imagingto explicitly model the beam path for each point on the sensor plane andgeneralize this paradigm to the beam path of each pixel, then use thefrustum-based volume rendering to render each pixel's beam path. We furtherdesign an efficient probabilistic training (p-training) strategy to simplifythe training process vastly. Extensive experiments demonstrate that ourNeRFocus can achieve various 3D defocus effects with adjustable camera pose,focus distance, and aperture size. Existing NeRF can be regarded as our specialcase by setting aperture size as zero to render large depth-of-field images.Despite such merits, NeRFocus does not sacrifice NeRF's original performance(e.g., training and inference time, parameter consumption, rendering quality),which implies its great potential for broader application and furtherimprovement. Code and video are available athttps://github.com/wyhuai/NeRFocus.

NeRFocus: Neural Radiance Field for 3D Synthetic Defocus