Neural Radiance Fields (NeRF) have received considerable attention recently, due to its impressive capability in photo-realistic 3D reconstruction and novel view synthesis, given a set of posed camera images. Earlier work usually assumes the input images are of good quality. However, image degradation (e.g. image motion blur in low-light conditions) can easily happen in real-world scenarios, which would further affect the rendering quality of NeRF. In this paper, we present a novel bundle adjusted deblur Neural Radiance Fields (BAD-NeRF), which can be robust to severe motion blurred images and inaccurate camera poses. Our approach models the physical image formation process of a motion blurred image, and jointly learns the parameters of NeRF and recovers the camera motion trajectories during exposure time. In experiments, we show that by directly modeling the real physical image formation process, BAD-NeRF achieves superior performance over prior works on both synthetic and real datasets. Code and data are available at https://github.com/WU-CVGL/BAD-NeRF.

翻译：神经辐射场（Neural Radiance Fields, NeRF）因其在给定一组带有位姿的相机图像条件下实现逼真三维重建和新视角合成的卓越能力，近期受到了广泛关注。早期工作通常假设输入图像具有良好质量。然而，现实场景中图像退化（例如低光环境下的运动模糊）极易发生，这将进一步影响NeRF的渲染质量。本文提出了一种新型光束法平差去模糊神经辐射场（BAD-NeRF），该方法对严重运动模糊图像及不准确相机位姿具有鲁棒性。我们的方法建模了运动模糊图像的物理成像过程，联合学习NeRF参数并恢复曝光时间内的相机运动轨迹。实验表明，通过直接建模真实物理成像过程，BAD-NeRF在合成数据集和真实数据集上均取得了优于先前工作的性能。代码及数据见 https://github.com/WU-CVGL/BAD-NeRF。