# 4D sensor perception in relativistic image processing

## TL;DR

This paper introduces a new method for position and depth estimation using 4D sensor data and relativity principles.

## Contribution

The novel approach integrates relativity and temporal data into 4D space processing for sensor perception.

## Key findings

- A 4D model with 10 degrees of freedom is used to process sensor and image data as a causal tensor field.
- The method enables temporal prediction of position and environmental changes while extracting depth and sensor maps.

## Abstract

This article introduces the 4D sensor perception in relativistic image processing as a novel way of position and depth estimation. Relativistic image processing extends conventional image processing in computer vision to include the theory of relativity and combines temporal sensor and image data. In consideration of these temporal and relativistic aspects, we process diverse types of information in a novel model of 4D space through 10 different degrees of freedom consisting of 4 translations and 6 rotations. In this way, sensor and image data can be related and processed as a causal tensor field. This enables the temporal prediction of a user’s own position and environmental changes as well as the extraction of depth and sensor maps by related sensors and images. The dynamic influences and cross-sensor dependencies are incorporated into the metric calculation of spatial distances and positions, opening up new perspectives on numerous fields of application in mobility, measurement technology, robotics, and medicine.