We consider the problem of 3D shape recovery from ultra-fast motion-blurred images. While 3D reconstruction from static images has been extensively studied, recovering geometry from extreme motion-blurred images remains challenging. Such scenarios frequently occur in both natural and industrial settings, such as fast-moving objects in sports (e.g., balls) or rotating machinery, where rapid motion distorts object appearance and makes traditional 3D reconstruction techniques like Multi-View Stereo (MVS) ineffective. In this paper, we propose a novel inverse rendering approach for shape recovery from ultra-fast motion-blurred images. While conventional rendering techniques typically synthesize blur by averaging across multiple frames, we identify a major computational bottleneck in the repeated computation of barycentric weights. To address this, we propose a fast barycentric coordinate solver, which significantly reduces computational overhead and achieves a speedup of up to 4.57x, enabling efficient and photorealistic simulation of high-speed motion. Crucially, our method is fully differentiable, allowing gradients to propagate from rendered images to the underlying 3D shape, thereby facilitating shape recovery through inverse rendering. We validate our approach on two representative motion types: rapid translation and rotation. Experimental results demonstrate that our method enables efficient and realistic modeling of ultra-fast moving objects in the forward simulation. Moreover, it successfully recovers 3D shapes from 2D imagery of objects undergoing extreme translational and rotational motion, advancing the boundaries of vision-based 3D reconstruction. Project page: https://maxmilite.github.io/rec-from-ultrafast-blur/

翻译：本文研究从超高速运动模糊图像中恢复三维形状的问题。尽管从静态图像进行三维重建已被广泛研究，但从极端运动模糊图像中恢复几何结构仍具挑战性。此类场景在自然环境和工业场景中频繁出现，例如体育运动中高速运动的物体（如球类）或旋转机械，其快速运动会扭曲物体外观，导致传统三维重建技术（如多视图立体视觉）失效。本文提出一种新颖的逆向渲染方法，用于从超高速运动模糊图像中恢复形状。传统渲染技术通常通过多帧平均来合成模糊效果，我们发现其中重心权重重复计算是主要计算瓶颈。为此，我们提出一种快速重心坐标求解器，显著降低计算开销并实现高达4.57倍的加速，从而能够高效且逼真地模拟高速运动。关键在于，我们的方法完全可微分，允许梯度从渲染图像传播到底层三维形状，进而通过逆向渲染促进形状恢复。我们在两种典型运动类型（快速平移与旋转）上验证了该方法。实验结果表明，我们的方法在前向模拟中能够高效且真实地建模超高速运动物体。更重要的是，该方法成功地从经历极端平移和旋转运动的物体二维图像中恢复了三维形状，推动了基于视觉的三维重建技术边界。项目页面：https://maxmilite.github.io/rec-from-ultrafast-blur/