Varjo Technologies Oy

Distance Technologies Oy

Varjo Technologies Dy

Petteri Timonen

Mikko Strandborg

Ville Timonen

Joni-Matti Määttä

Mikko Ollila

Ari Antti Erik Peuhkurinen

Joona Petrell

Oleksandr Dovzhenko

Roman Golovanov

Tuomas Tölli

Pekka Väänänen

Tarek Mohsen

A first image and a second image to be directed toward a first eye and a second eye, respectively, are generated or retrieved. For a given first segment of the first image presenting at least a part of a virtual object, an amount of crosstalk toward the first eye is determined based on at least the second image. A given second segment of the second image that also presents at least said part of the virtual object is determined. Intensity values of pixels in the given second segment of the second image are adjusted, based on the amount of crosstalk determined for the given first segment of the first image. An autostereoscopic image is then generated using the first image and the second image, and displayed via an autostereoscopic display.

Crosstalk compensation in autostereoscopic displays using binocular summation

A 3D bounding volume within which a graphical element is to be rasterized for a given eye is determined. A set of light-emitting elements from which light is directed toward the given eye along a direction intersecting the 3D bounding volume is identified. For each light-emitting element in the set, its perceived shape and angular size from a perspective of the given eye is determined. The graphical element is rasterized onto the set of light-emitting elements based at least on relative perceived positions of the light-emitting elements from the perspective of the given eye, and their perceived shape and their perceived angular size. A colour value is computed for each light-emitting element in the set based on the rasterization of the graphical element. A synthetic light field is then produced displaying the graphical element, using computed colour values for the light-emitting elements in the set.

Pixel-accurate graphics rasterization for multiscopic displays

A system includes a server and a data repository storing a three-dimensional environment model, where the server is configured to: receive, from a client device a first image of a real-world environment captured by camera along with information indicative of a first measured pose of the client device; utilise the 3D environment model to generate a first reconstructed image from a perspective of a first measured pose; determine a transformation indicative of a difference in the first measured pose and a first actual pose of the client device; use the transformation to calculate the first actual pose; and send information indicative of at least one of: the first actual pose, or the first spatial transformation, to the client device for calculating subsequent actual poses.

Using cloud computing to improve accuracy of pose tracking

A computer-implemented method including: capturing visible-light images via visible-light camera(s) from view points in real-world environment, wherein 3D positions of view points are represented in coordinate system; dividing 3D space of real-world environment into 3D grid of convex-polyhedral regions; creating 3D data structure including nodes representing convex-polyhedral regions of 3D space; determining 3D positions of pixels of visible-light images based on 3D positions of view points; dividing each visible-light image into portions, wherein 3D positions of pixels of given portion of said visible-light image fall inside corresponding convex-polyhedral region; and storing, in each node, portions of visible-light images whose pixels' 3D positions fall inside corresponding convex-polyhedral region, wherein each portion of visible-light image is stored in corresponding node.

Image-based environment reconstruction

An imaging system includes first camera; second camera, second field of view of second camera being wider than first field of view of first camera, wherein first field of view overlaps with portion of second field of view; and processor(s) configured to: capture first images and second images, wherein overlapping image segment and non-overlapping image segment of second image correspond to said portion and remaining portion of second field of view; determine blurred region(s) (B, B) of first image; and generate output image in manner that: inner image segment of output image is generated from: region(s) of overlapping image segment that corresponds to blurred region(s) of first image, and remaining region of first image that is not blurred, and peripheral image segment of output image is generated from non-overlapping image segment.

Image blending

A method including: receiving visible-light images captured using camera(s) and depth data corresponding to said images; identifying image segments of visible-light image that represent objects or their parts belonging to different material categories; detecting whether at least two adjacent image segments in visible-light image pertain to at least two different material categories related to same object category; and when it is detected that at least two adjacent image segments pertain to at least two different material categories related to same object category, identifying at least two adjacent depth segments of depth data corresponding to at least two adjacent image segments; and correcting errors in optical depths represented in at least one of at least two adjacent depth segments, based on optical depths represented in remaining of at least two adjacent depth segments.

Depth maps and 3D reconstruction with segmentation masks

A system including server(s) configured to: receive, from host device, visible-light images of real-world environment captured by visible-light camera(s); process visible-light images to generate three-dimensional (3D) environment model; receive, from client device, information indicative of pose of client device; utilise 3D environment model to generate reconstructed image(s) and reconstructed depth map(s); determine position of each pixel of reconstructed image(s); receive, from host device, current visible-light image(s); receive, from host device, information indicative of current pose of host device, or determine said current pose; determine, for pixel of reconstructed image(s), whether or not corresponding pixel exists in current visible-light image(s); replace initial pixel values of pixel in reconstructed image(s) with pixel values of corresponding pixel in current visible-light image(s), when corresponding pixel exists in current visible-light image(s); and send reconstructed image(s) to client device.

Using camera feed to improve quality of reconstructed images

An imaging system including visible-light camera(s), depth sensor(s), pose-tracking means, and server(s) configured to: control visible-light camera(s) and depth sensor(s) to capture visible-light images and depth images of real-world environment, respectively, whilst processing pose-tracking data to determine poses of visible-light camera(s) and depth sensor(s); reconstruct three-dimensional lighting model of real-world environment representative of lighting in different regions of real-world environment; receive, from client application, request message comprising information indicative of location in real-world environment where virtual object(s) is to be placed; utilise three-dimensional lighting model to create sample lighting data for said location, wherein sample lighting data is representative of lighting at given location in real-world environment; and provide client application with sample lighting data.

Imaging systems and methods for facilitating local lighting

A display system including display or projector , camera, means for tracking position and orientation of user's head, and processor. The processor is configured to control camera to capture images of real-world environment using default exposure setting, whilst processing head-tracking data to determine corresponding positions and orientations of user's head with respect to which images are captured; process images to create environment map of real-world environment; generate extended-reality image from images using environment map; render extended-reality image; adjust exposure of camera to capture underexposed image of real-world environment; process images to generate derived image; generate next extended-reality image from derived image using environment map; render next extended-reality image; and identify and modify intensities of oversaturated pixels in environment map, based on underexposed image and position and orientation with respect to which underexposed image is captured.

Growing community of inventors

Helsinki, Finland

Petteri Timonen