Magic Leap, Inc.

Meta Platforms Technologies, LLC

Elad Joseph

David Cohen

Ali Shahrokni

Eyal Preter

Gal Braun

Giora Yahav

Eitan Shmuel Bar-On

Ron Nisim Ferens

Andrew Rabinovich

Vijay Badrinarayanan

Koon Keong Shee

Zhao Chen

Evyatar Bluzer

Paul Lacey

Shanghsuan Tsai

Ezri Joseph Sonn

A distributed, cross reality system efficiently and accurately compares location information that includes image frames. Each of the frames may be represented as a numeric descriptor that enables identification of frames with similar content. The resolution of the descriptors may vary for different computing devices in the distributed system based on degree of ambiguity in image comparisons and/or computing resources for the device. A descriptor computed for a cloud-based component operating on maps of large areas that can result in ambiguous identification of multiple image frames may use high resolution descriptors. High resolution descriptors reduce computationally intensive disambiguation processing. A portable device, which is more likely to operate on smaller maps and less likely to have the computational resources to compute a high resolution descriptor, may use a lower resolution descriptor.

Cross reality system with map processing using multi-resolution frame descriptors

A display system can include a head-mounted display configured to project light to an eye of a user to display virtual image content at different amounts of divergence and collimation. The display system can include an inward-facing imaging system possibly comprising a plurality of cameras that image the user's eye and glints for thereon and processing electronics that are in communication with the inward-facing imaging system and that are configured to obtain an estimate of a center of rotation of the user's eye using cornea data derived from the glint images. The display system may render virtual image content with a render camera positioned at the determined position of the center of rotation of said eye.

Eye center of rotation determination with one or more eye tracking cameras

Techniques are disclosed for operating a time-of-flight (TOF) sensor. The TOF may be operated in a low power mode by repeatedly performing a low power mode sequence, which may include performing a depth frame by emitting light pulses, detecting reflected light pulses, and computing a depth map based on the detected reflected light pulses. Performing the low power mode sequence may also include performing an amplitude frame at least one time by emitting a light pulse, detecting a reflected light pulse, and computing an amplitude map based on the detected reflected light pulse. In response to determining that an activation condition is satisfied, the TOF may be switched to operate in a high accuracy mode by repeatedly performing a high accuracy mode sequence, which may include performing the depth frame multiple times.

Power-efficient hand tracking with time-of-flight sensor

A method includes a computing system associated with a first artificial-reality device accessing sensor data of a real environment, generating a first plurality of feature descriptors for the real environment, and generating a second plurality of feature descriptors for the real environment based on the first plurality of feature descriptors, wherein the second plurality of feature descriptors have lower detail than the first plurality of feature descriptors. The computing system may further cause the first plurality of feature descriptors to be transmitted using a wireless connection, wherein the transmitted first plurality of feature descriptors are configured to be converted into a third plurality of feature descriptors different from the second plurality of feature descriptors. The third plurality of feature descriptors are configured to be used by a second artificial-reality device for tracking the real environment. The computing system tracks the real environment using the second plurality of feature descriptors.

Methods and systems to allow three-dimensional map sharing between heterogeneous computing systems, cross-localization, and sharing content in three-dimensional space

Systems and methods are disclosed for computing depth maps. One method includes capturing, using a camera, a camera image of a runtime scene. The method may also include analyzing the camera image of the runtime scene to determine a plurality of target sampling points at which to capture depth of the runtime scene. The method may further include adjusting a setting associated with a low-density depth sensor based on the plurality of target sampling points. The method may further include capturing, using the low-density depth sensor, a low-density depth map of the runtime scene at the plurality of target sampling points. The method may further include generating a computed depth map of the runtime scene based on the camera image of the runtime scene and the low-density depth map of the runtime scene.

Image-enhanced depth sensing via depth sensor control

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Haifa, Israel

Elad Joseph