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Event-Based 3D Motion Flow Estimation Using 4D Spatio Temporal Subspaces Properties.

Sio-Hoi Ieng1, João Carneiro1, Ryad B Benosman1

  • 1Institut National de la Santé et de la Recherche Médicale, UMRI S 968; Sorbonne Université, University of Pierre and Marie Curie, Univ Paris 06, UMR S 968; Centre National de la Recherche Scientifique, UMR 7210, Institut de la Vision Paris, France.

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Summary
This summary is machine-generated.

This study presents a novel time-based method for estimating scene flow from 3D point clouds, applicable to both synchronous and asynchronous data from various sensors. The approach effectively handles deformable objects and leverages neuromorphic vision for continuous 3D reconstruction.

Keywords:
3D point cloudsevent-based sensingmotion estimationmotion from structureneuromorphic visionscene flow

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Fusion

Background:

  • Current scene flow estimation relies on image luminance, limiting its application.
  • Neuromorphic sensors offer asynchronous, continuous data streams.

Purpose of the Study:

  • To develop a purely time-based scene flow estimation method for 3D point clouds.
  • To create a unifying framework for synchronous and asynchronous 3D data.
  • To enable scene flow estimation without luminance information.

Main Methods:

  • Formulating scene flow estimation using local piecewise regularization.
  • Utilizing 4D space-time properties and subspace decomposition.
  • Exploiting asynchronous event-based vision sensor data for continuous reconstruction.

Main Results:

  • Successful scene flow estimation from synchronous and asynchronous 3D point clouds.
  • Demonstrated capability to handle deformable object motion.
  • Validated performance across diverse 3D sensors.

Conclusions:

  • The proposed time-based method offers a robust alternative to luminance-dependent techniques.
  • The framework effectively integrates data from various 3D sensors, including neuromorphic ones.
  • This approach advances scene flow estimation for dynamic and complex environments.