Recovering 3D Shapes from Ultra-Fast Motion-Blurred Images

TL;DR

This paper introduces a fast, differentiable inverse rendering method that recovers 3D shapes from ultra-fast motion-blurred images, overcoming traditional challenges posed by extreme motion distortions.

Contribution

It presents a novel inverse rendering approach with a fast barycentric coordinate solver, enabling efficient 3D shape recovery from highly blurred images caused by rapid motion.

Findings

Achieves up to 4.57x speedup in rendering computations

Successfully recovers 3D shapes from images with extreme motion blur

Demonstrates effectiveness on translational and rotational motion types

Abstract

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/