Systems and methods described herein relate to self-supervised scale-aware learning of camera extrinsic parameters. One embodiment processes instantaneous velocity between a target image and a context image captured by a first camera; jointly training a depth network and pose network based on scaling by the instantaneous velocity; produce depth map using the depth network; produce ego-motion of the first camera using the pose network; generate synthesized image from the target image using a reprojection operation based on the depth map, the ego-motion, the context image and camera intrinsics; determine photometric loss by comparing the synthesized image to the target image; generate photometric consistency constraint using a gradient from the photometric loss; determine pose consistency constraint between the first camera and a second camera; and optimize the photometric consistency constraint, the pose consistency constraint, the depth network and the pose network to generate estimated extrinsic parameters.


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    Title :

    SELF EXTRINSIC SELF-CALIBRATION VIA GEOMETRICALLY CONSISTENT SELF-SUPERVISED DEPTH AND EGO-MOTION LEARNING


    Contributors:

    Publication date :

    2025-03-20


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06V / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion



    PHOTOMETRIC MASKS FOR SELF-SUPERVISED DEPTH LEARNING

    GUIZILINI VITOR | European Patent Office | 2024

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