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Neural Radiance Fields (NeRF) has received much attention recently due to itsimpressive capability to represent 3D scene and synthesize novel view images.Existing works usually assume that the input images are captured by a globalshutter camera. Thus, rolling shutter (RS) images cannot be trivially appliedto an off-the-shelf NeRF algorithm for novel view synthesis. Rolling shuttereffect would also affect the accuracy of the camera pose estimation (e.g. viaCOLMAP), which further prevents the success of NeRF algorithm with RS images.In this paper, we propose Unrolling Shutter Bundle Adjusted Neural RadianceFields (USB-NeRF). USB-NeRF is able to correct rolling shutter distortions andrecover accurate camera motion trajectory simultaneously under the framework ofNeRF, by modeling the physical image formation process of a RS camera.Experimental results demonstrate that USB-NeRF achieves better performancecompared to prior works, in terms of RS effect removal, novel view imagesynthesis as well as camera motion estimation. Furthermore, our algorithm canalso be used to recover high-fidelity high frame-rate global shutter video froma sequence of RS images.

USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields