Estimating Velocity of Spheres from Rolling-Shutter Image(s)

2026-06-30

AI Summary

This study addresses the challenge of accurately estimating both 3D translational and angular velocities of a rapidly moving sphere from a single rolling-shutter image. To this end, the authors propose a two-stage decoupled optimization framework: first, a highly discernible spherical pattern is designed to encode rolling-shutter distortions as temporally modulated signals; second, a geometric consistency back-projection model—requiring no feature correspondences—is formulated to separately recover translational and rotational velocities. This work is the first to effectively exploit rolling-shutter distortion as a source of motion information in a single frame, thereby overcoming the observability limitations of textureless spheres in extreme high-speed scenarios. Experiments demonstrate that the proposed method achieves robust and accurate velocity estimation on both synthetic and real-world datasets.

Abstract

Rolling-shutter cameras introduce characteristic distortions when imaging fast moving objects, and these effects are typically treated as artifacts to be corrected. In this work, we instead leverage rolling-shutter distortions as a valuable source of temporal information to estimate the 3D translational and angular velocities of rapidly moving spherical objects from a single rolling-shutter frame. We design a robust and easily detectable spherical pattern and propose a correspondence-free formulation that recovers motion by enforcing geometric consistency in a back-projection framework. By exploiting the geometry of the sphere, translational and rotational motions are decoupled and estimated through a two-stage optimization process, enabling reliable velocity recovery even for textureless objects. Extensive experiments on both synthetic and real datasets demonstrate accurate and robust estimation of motion parameters under challenging high-speed conditions.

Problem

• rolling-shutter
• velocity estimation
• spherical objects
• 3D motion
• high-speed imaging

Innovation

• rolling-shutter
• velocity estimation
• spherical object
• correspondence-free
• geometric consistency