Estimating Velocity and Spin of Spherical Objects from Rolling-Shutter Image(s)

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