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公开(公告)号：US20240331366A9
公开(公告)日：2024-10-03
申请号：US18526787
申请日：2023-12-01
Applicant: Torc Robotics, Inc.
Inventor： Emmanuel Luc Julien Onzon , Felix Heide , Maximilian Rufus Bömer , Fahim Mannan
CPC classification number: G06V10/776 , G06T5/40 , G06T5/50 , G06T7/11 , G06V10/7715 , G06V10/806 , G06V10/955 , G06V20/38 , G06T2207/10144 , G06T2207/20161
Abstract: A computer-vision pipeline is organized as a closed loop of a sensor-processing phase, an image-processing phase, and an object-detection phase, each comprising a respective phase processor coupled to a master processor. The sensor-processing phase creates multiple exposure images, and derives multi-exposure multi-scale zonal illumination-distributions, to be processed independently in the image-processing phase. In a first implementation of the object-detection phase, extracted exposure-specific features are pooled prior to overall object detection. In a second implementation, exposure-specific objects, detected from the exposure-specific features, are fused to produce the sought objects of a scene under consideration. The two implementations enable detecting fine details of a scene under diverse illumination conditions. The master processor performs loss-function computations to derive updated training parameters of the processing phases. Several experiments applying a core method of operating the computer-vision pipelines, and variations thereof, ascertain performance gain under challenging illumination conditions.
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2.发明公开公开(公告)号：US20240233351A1
公开(公告)日：2024-07-11
申请号：US18545874
申请日：2023-12-19
Applicant: Torc Robotics, Inc.
Inventor： Emmanuel Luc Julien Onzon , Felix Heide , Maximilian Rufus Bömer , Fahim Mannan
CPC classification number: G06V10/806 , G06T5/70 , G06V10/60 , G06V10/82
Abstract: Departing from conventional HIDR image fusion approach, a learned task-driven fusion in the feature domain is disclosed. Instead of using a single companded image, the disclosed method exploits semantic features from all exposures learned in an end-to-end fashion with supervision from downstream detection losses. The method outperforms all tested conventional HDR exposure fusion and auto-exposure methods in challenging automotive HIDR scenarios.
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公开(公告)号：US20240127584A1
公开(公告)日：2024-04-18
申请号：US18526787
申请日：2023-12-01
Applicant: Torc Robotics, Inc.
Inventor： Emmanuel Luc Julien Onzon , Felix Heide , Maximilian Rufus Bömer , Fahim Mannan
CPC classification number: G06V10/776 , G06T5/40 , G06T5/50 , G06T7/11 , G06V10/7715 , G06V10/806 , G06V10/955 , G06V20/38 , G06T2207/10144 , G06T2207/20161
Abstract: A computer-vision pipeline is organized as a closed loop of a sensor-processing phase, an image-processing phase, and an object-detection phase, each comprising a respective phase processor coupled to a master processor. The sensor-processing phase creates multiple exposure images, and derives multi-exposure multi-scale zonal illumination-distributions, to be processed independently in the image-processing phase. In a first implementation of the object-detection phase, extracted exposure-specific features are pooled prior to overall object detection. In a second implementation, exposure-specific objects, detected from the exposure-specific features, are fused to produce the sought objects of a scene under consideration. The two implementations enable detecting fine details of a scene under diverse illumination conditions. The master processor performs loss-function computations to derive updated training parameters of the processing phases. Several experiments applying a core method of operating the computer-vision pipelines, and variations thereof, ascertain performance gain under challenging illumination conditions.
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