We present ExBluRF, a novel view synthesis method for extreme motion blurred images based on efficient radiance fields optimization. Our approach consists of two main components: 6-DOF camera trajectory-based motion blur formulation and voxel-based radiance fields. From extremely blurred images, we optimize the sharp radiance fields by jointly estimating the camera trajectories that generate the blurry images. In training, multiple rays along the camera trajectory are accumulated to reconstruct single blurry color, which is equivalent to the physical motion blur operation. We minimize the photo-consistency loss on blurred image space and obtain the sharp radiance fields with camera trajectories that explain the blur of all images. The joint optimization on the blurred image space demands painfully increasing computation and resources proportional to the blur size. Our method solves this problem by replacing the MLP-based framework to low-dimensional 6-DOF camera poses and voxel-based radiance fields. Compared with the existing works, our approach restores much sharper 3D scenes from challenging motion blurred views with the order of 10 times less training time and GPU memory consumption.

翻译：我们提出 ExBluRF，一种基于高效辐射场优化的极端运动模糊图像新视角合成方法。该方法包含两个核心组件：基于六自由度相机轨迹的运动模糊建模，以及基于体素的辐射场表示。通过极端模糊图像，我们联合估计生成模糊图像的相机轨迹，同时优化清晰的辐射场。训练过程中，沿相机轨迹的多条光线被累积以重构单一模糊颜色，该过程等效于物理运动模糊操作。我们在模糊图像空间上最小化光度一致性损失，从而获得可解释所有图像模糊的清晰辐射场及对应的相机轨迹。模糊图像空间上的联合优化需要与模糊规模成比例增长的庞大计算量与资源消耗。我们的方法通过将基于 MLP 的框架替换为低维六自由度相机位姿与基于体素的辐射场解决了该问题。与现有工作相比，本方法能从具有挑战性的运动模糊视角中恢复出更清晰的 3D 场景，同时训练时间和 GPU 内存消耗降低约一个数量级。