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

• URL: http://arxiv.org/abs/2606.31760v2
• Date: Wed, 01 Jul 2026 16:12:03 GMT
• Title: Estimating Velocity and Spin of Spherical Objects from Rolling-Shutter Image(s)
• Abstract summary: Rolling-shutter cameras introduce characteristic distortions when imaging fast moving objects.<n>We estimate the 3D translational and angular velocities of rapidly moving spherical objects from a single rolling-shutter frame.
• Score: 3.2378254336734518
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• ASTRA: Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignment [86.35109211408489]
  We propose ASTRA, a framework that introduces 2D motion trajectories as explicit, texture-agnostic supervision for asynchronous dynamic reconstruction.<n>ASTRA preserves high-frequency spatial details and sustains strong robustness even under severe asynchrony with up to 25-frame offsets, achieving approximately 1.4 dB PSNR improvement, reducing temporal-offset MAE by 54.0%, and nearly quadrupling the synchronization success rate.
  arXiv  Detail & Related papers  (2026-08-03T10:06:45Z)
• Rotational Motion-Induced Error Compensation for Phase-Shifting Profilometry-Based Eye Reconstruction [0.7933039558471408]
  This study proposes a rotational motion compensation framework for PSP-based dynamic 3D eye reconstruction.<n>Relative eye rotation is estimated from image-based motion cues using a user-specific 3D eye model in a spherical-coordinate domain.<n> Experiments with a rotating fake eye under non-uniform motion demonstrate that the proposed method substantially suppresses motion-induced deformation and improves reconstruction accuracy.
  arXiv  Detail & Related papers  (2026-07-16T11:46:44Z)
• Event-based SLAM Benchmark for High-Speed Maneuvers [68.148886127117]
  Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution.<n>Existing event-based approaches, although successful in mitigating motion blur caused by high-speed maneuvers, suffer from many limitations.<n>We introduce a benchmarking framework for event-based state estimation, called EvSLAM, characterized by sufficient variation in data collection platforms.
  arXiv  Detail & Related papers  (2026-04-27T04:36:41Z)
• Detailed Geometry and Appearance from Opportunistic Motion [44.38825288921379]
  Reconstructing 3D geometry and appearance from a sparse set of fixed cameras is a foundational task with broad applications.<n>We show that the static cameras effectively orbit'' the object in its local coordinate frame, providing additional virtual viewpoints.<n> Harnessing this object motion, however, poses two challenges: the tight coupling of object pose and geometry estimation and the complex appearance variations of a moving object under static illumination.
  arXiv  Detail & Related papers  (2026-03-27T17:59:16Z)
• Robo3R: Enhancing Robotic Manipulation with Accurate Feed-Forward 3D Reconstruction [70.06600045165905]
  3D spatial perception is fundamental to general robotic manipulation, yet obtaining reliable, high-quality 3D geometry remains challenging.<n>We introduce Robo3R, a manipulation-ready 3D reconstruction model that predicts accurate, metric-scale scene geometry directly from RGB images and robot states in real time.<n>We observe consistent gains in performance, suggesting the promise of this alternative 3D sensing module for robotic manipulation.
  arXiv  Detail & Related papers  (2026-02-10T18:58:15Z)
• 3D Trajectory Reconstruction of Moving Points Based on Asynchronous Cameras [6.9017898687323775]
  Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.<n>This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras.
  arXiv  Detail & Related papers  (2025-05-31T13:04:31Z)
• EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation [59.33052312107478]
  Event cameras offer possibilities for 3D motion estimation through continuous adaptive pixel-level responses to scene changes.<n>This paper presents EMove, a novel event-based framework that models-uniform trajectories via event-guided parametric curves.<n>For motion representation, we introduce a density-aware adaptation mechanism to fuse spatial and temporal features under event guidance.<n>The final 3D motion estimation is achieved through multi-temporal sampling of parametric trajectories, flows and depth motion fields.
  arXiv  Detail & Related papers  (2025-03-14T13:15:54Z)
• CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images [19.08403715388913]
  3D Gaussian Splatting has gained significant attention due to its high-quality novel view rendering.<n>A critical issue is the camera motion blur caused by movement during exposure, which hinders accurate 3D scene reconstruction.<n>We propose CoMoGaussian, a Continuous Motion-Aware Gaussian Splatting that reconstructs precise 3D scenes from motion-blurred images.
  arXiv  Detail & Related papers  (2025-03-07T11:18:43Z)
• MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking [58.719310295870024]
  This paper presents an event-based framework for tracking any point.<n>To resolve ambiguities caused by event sparsity, a motion-guidance module incorporates kinematic vectors into the local matching process.<n>The method improves the $Survival_50$ metric by 17.9% over event-only tracking of any point baseline.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion-Blurred Images [14.738528284246545]
  CRiM-GS is a textbfContinuous textbfRigid textbfMotion-aware textbfGaussian textbfSplatting.<n>It reconstructs precise 3D scenes from motion-blurred images while maintaining real-time rendering speed.
  arXiv  Detail & Related papers  (2024-07-04T13:37:04Z)
• Event-based Structure-from-Orbit [23.97673114572094]
  Certain applications in robotics and vision-based navigation require 3D perception of an object undergoing circular or spinning motion in front of a static camera. We propose event-based structure-from-orbit (eSf), where the aim is to reconstruct the 3D structure of a fast spinning object observed from a static event camera.
  arXiv  Detail & Related papers  (2024-05-10T03:02:03Z)
• SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model [63.685132323224124]
  Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains. In this paper, we introduce a novel framework of SphereDiffusion to address these unique challenges. Experiments on Structured3D dataset show that SphereDiffusion significantly improves the quality of controllable spherical image generation and relatively reduces around 35% FID on average.
  arXiv  Detail & Related papers  (2024-03-15T06:26:46Z)
• Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos [115.71874459429381]
  We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. Experiments on benchmark datasets demonstrate that our method outperforms previous methods for fast moving object deblurring and 3D reconstruction.
  arXiv  Detail & Related papers  (2021-11-29T11:25:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.