公开(公告)号：US12242667B2

公开(公告)日：2025-03-04

申请号：US18480181

申请日：2023-10-03

Applicant: Microsoft Technology Licensing, LLC

Inventor： Curtis Alan Tesdahl ,  Benjamin Eliot Lundell ,  David Rohn ,  Dmitry Reshidko ,  Dmitriy Churin ,  Kevin James Matherson ,  Sayyed Jaffar Ali Raza

IPC: G06F3/01 ,  G02B27/00 ,  G02B27/01 ,  G06N3/084 ,  G06V10/82 ,  G06V40/10 ,  G06V40/18

Abstract: Eye and hand tracking systems in head-mounted display (HMD) devices are arranged with lensless camera systems using optical masks as encoding elements that apply convolutions to optical images of body parts (e.g., eyes or hands) of HMD device users. The convolved body images are scrambled or coded representations that are captured by a sensor in the system, but are not human-recognizable. A machine learning system such as a neural network is configured to extract body features directly from the coded representation without performance of deconvolutions conventionally utilized to reconstruct the original body images in human-recognizable form. The extracted body features are utilized by the respective eye or hand tracking systems to output relevant tracking data for the user's eyes or hands which may be utilized by the HMD device to support various applications and user experiences. The lensless camera and machine learning system are jointly optimizable on an end-to-end basis.

公开(公告)号：US11803238B1

公开(公告)日：2023-10-31

申请号：US17832424

申请日：2022-06-03

Applicant: Microsoft Technology Licensing, LLC

Inventor： Curtis Alan Tesdahl ,  Benjamin Eliot Lundell ,  David Rohn ,  Dmitry Reshidko ,  Dmitriy Churin ,  Kevin James Matherson ,  Sayyed Jaffar Ali Raza

IPC: G06F3/01 ,  G02B27/00 ,  G06N3/084 ,  G06V40/10 ,  G06V40/18 ,  G06V10/82 ,  G02B27/01

CPC classification number: G06F3/013 ,  G02B27/0093 ,  G02B27/0101 ,  G02B27/017 ,  G06N3/084 ,  G06V10/82 ,  G06V40/11 ,  G06V40/193 ,  G02B2027/014 ,  G02B2027/0138

Abstract: Eye and hand tracking systems in head-mounted display (HMD) devices are arranged with lensless camera systems using optical masks as encoding elements that apply convolutions to optical images of body parts (e.g., eyes or hands) of HMD device users. The convolved body images are scrambled or coded representations that are captured by a sensor in the system, but are not human-recognizable. A machine learning system such as a neural network is configured to extract body features directly from the coded representation without performance of deconvolutions conventionally utilized to reconstruct the original body images in human-recognizable form. The extracted body features are utilized by the respective eye or hand tracking systems to output relevant tracking data for the user's eyes or hands which may be utilized by the HMD device to support various applications and user experiences. The lensless camera and machine learning system are jointly optimizable on an end-to-end basis.