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4D sensor perception in relativistic image processing.

Simone Müller1, Dieter Kranzlmüller2

  • 1Leibniz Supercomputing Centre (LRZ), Center for Virtual Reality and Visualisation (V2C), Munich, 85748, Germany. simone.mueller@lrz.de.

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|February 18, 2025
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Summary
This summary is machine-generated.

This study presents 4D sensor perception using relativistic image processing for accurate position and depth estimation. It enables temporal prediction and mapping by integrating sensor and image data within a 4D space model.

Keywords:
4D Informationen4D Sensor PerceptionRelativistic Image ProcessingSchlingel Diagram

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Area of Science:

  • Computer Vision
  • Relativistic Physics
  • Sensor Data Fusion

Background:

  • Conventional image processing lacks temporal and relativistic considerations.
  • Integrating sensor and image data requires advanced models.

Purpose of the Study:

  • Introduce 4D sensor perception in relativistic image processing.
  • Enable accurate position and depth estimation.
  • Facilitate temporal prediction and environmental change analysis.

Main Methods:

  • Extended conventional image processing with relativity theory.
  • Combined temporal sensor and image data.
  • Utilized a 4D space model with 10 degrees of freedom (4 translations, 6 rotations).
  • Processed data as a causal tensor field.

Main Results:

  • Enabled temporal prediction of user position and environmental changes.
  • Extracted depth and sensor maps from fused data.
  • Incorporated dynamic influences and cross-sensor dependencies into spatial metric calculations.

Conclusions:

  • 4D sensor perception offers a novel approach to position and depth estimation.
  • The method provides new perspectives for applications in mobility, measurement technology, robotics, and medicine.