Analysis portion, time-of-flight imaging device and method

Abstract

The present disclosure pertains to an analysis portion for a time-of-flight imaging portion, wherein the time-of-flight imaging portion includes at least one imaging element of a first type and at least one imaging element of a second type, wherein the at least one imaging element of the first type and the at least one imaging element of the second type are arranged in a predetermined pattern, configured to: construct first imaging data of the at least one imaging element of the first type based on second imaging element data of the at least one imaging element of the second type, wherein the first imaging data are constructed based on a machine learning algorithm.

Description

technical field [0001] The present disclosure generally relates to an analysis section for a time-of-flight imaging section, a time-of-flight imaging device and a method for controlling a time-of-flight imaging section. Background technique [0002] In general, time-of-flight (ToF) devices are known for eg imaging or creating a depth map of a scene such as an object, a person, or the like. A distinction can be made between direct ToF (dToF) and indirect ToF (iToF) to measure distance by measuring the transit time (dToF) of emitted and reflected light or by measuring one or more phase shifts (iToF) of emitted and reflected light. [0003] To measure distance, known time-of-flight devices need to go through thousands or millions of measurement cycles, which can result in a time-consuming process. Furthermore, in order to reduce the number of measurement cycles while maintaining a complex imaging chip that is also able to acquire information other than depth / distance informati...

AI Technical Summary

Problems solved by technology

[0003] To measure distance, time-of-flight devices are known to traverse thousands or millions of measurement cycles, which can result in a time-consuming process

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