论文标题
BAD-NERF：捆绑调整后的Deblur神经辐射场
BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields
论文作者
论文摘要
鉴于一组摆姿势的相机图像，由于其在照片真实的3D重建和新型视图综合方面，神经辐射场（NERF）最近受到了很大的关注。较早的工作通常假定输入图像质量良好。但是，在现实情况下，很容易发生图像降解（例如，在弱光条件下的图像运动模糊）很容易发生，这将进一步影响NERF的渲染质量。在本文中，我们提出了一个新颖的束调整后的Deblur神经辐射场（BAD-NERF），可以对严重的运动模糊图像和不准确的摄像头姿势进行稳健。我们的方法对运动模糊的物理图像形成过程进行建模，并共同学习NERF的参数并在曝光期间恢复相机运动轨迹。在实验中，我们表明，通过直接建模真实的物理图像形成过程，Bad-nerf可以在合成数据集和真实数据集上实现优越的性能。代码和数据可在https://github.com/wu-cvgl/bad-nerf上找到。
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.
