USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields

TL;DR

USB-NeRF introduces a novel method to correct rolling shutter distortions in neural radiance fields, enabling accurate 3D scene reconstruction, view synthesis, and camera motion estimation from RS images.

Contribution

It models the physical process of RS cameras within NeRF, allowing simultaneous correction of distortions and accurate camera trajectory recovery.

Findings

Outperforms prior methods in RS distortion removal

Improves quality of novel view synthesis from RS images

Enables high-fidelity global shutter video reconstruction

Abstract

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