Neural Radiance Field (NeRF) and its variants have exhibited great success on
representing 3D scenes and synthesizing photo-realistic novel views. However,
they are generally based on the pinhole camera model and assume all-in-focus
inputs. This limits their applicability as images captured from the real world
often have finite depth-of-field (DoF). To mitigate this issue, we introduce
DoF-NeRF, a novel neural rendering approach that can deal with shallow DoF
inputs and can simulate DoF effect. In particular, it extends NeRF to simulate
the aperture of lens following the principles of geometric optics. Such a
physical guarantee allows DoF-NeRF to operate views with different focus
configurations. Benefiting from explicit aperture modeling, DoF-NeRF also
enables direct manipulation of DoF effect by adjusting virtual aperture and
focus parameters. It is plug-and-play and can be inserted into NeRF-based
frameworks. Experiments on synthetic and real-world datasets show that,
DoF-NeRF not only performs comparably with NeRF in the all-in-focus setting,
but also can synthesize all-in-focus novel views conditioned on shallow DoF
inputs. An interesting application of DoF-NeRF to DoF rendering is also
demonstrated. The source code will be made available at
this https URL

介绍了一种称为 DoF-NeRF 的新颖神经渲染方法，能够处理浅景深图像输入并模拟景深效果，通过直接调整虚拟光圈和对焦参数可以进行景深效果的直接操作。