Apple Inc.

Meta Platforms, Inc.

Mapillary Ab

Facebook, Inc.

Jan Erik Solem

Yubin Kuang

Jerremy Holland

Pau Gargallo Piracés

Johan Gyllenspetz

Avi Elias Cieplinski

Rudolph Van Der Merwe

Garrett M Johnson

Russell Y Webb

Russell Mackay Reid

Michael Rousson

Peter Neubauer

Jerome Piovano

Fredrik Kahl

Yang Liu

Scott Thomas Smith

Myra Mary Haggerty

Jaron Waldman

William Matthew Vieta

Samuel Noble

Alex Tremain Nelson

Xiaqing Wu

John Kassebaum

Edward Allen Valko

Mikael Rousson

Matt Waldon

Karen Louise Jenkins

William E Hensler

Roger Marí Molas

Manuel Antonio López Antequera

A method for analysis of an image comprises: receiving () the image to be analyzed; processing () the image with a machine-learned model, wherein the machine-learned model is configured to predict at least an intrinsic parameter of the image using at least a first variable of the machine-learned model, wherein the first variable defines a relation between a radial distortion of the image and a focal length of the image; and outputting () the intrinsic parameter of the image. Also, methods for forming a 3D reconstruction of a scenery, for training a machine-learned model for analysis of an image and for generating a dataset of images for training a machine-learned model are disclosed.

Methods for analysis of an image and a method for generating a dataset of images for training a machine-learned model

A geographical area, which forms an area to be represented by a model of at least a portion of a scenery, is defined. A plurality of new images depicting the scenery is received. The plurality of new images is acquired by a camera from one or more positions within the geographical area. It is determined whether the plurality of new images fulfill a pre-defined updating criterion. In response to a determination that the plurality of new images fulfills the updating criterion, an update of the model is initiated. A boundary area enclosing the plurality of new images is defined. The boundary area is defined based at least in part on a distance margin between an edge of the boundary area and a new image included in the plurality of new images that is closest to the edge of the boundary area.

Method in constructing a model of a scenery and device therefor

A method in constructing a model of a scenery comprises: receiving () a plurality of images depicting the scenery, wherein each image is acquired by a camera () from a position at a rotational angle; creating () a plurality of sub-models, wherein each sub-model is created by: selecting () a basis image; determining () images related to the basis image; and determining () the sub-model based on the basis image and the related images, wherein the sub-model defines relative positions and rotational angles of the camera () for the basis image and the related images and the sub-model further defines three-dimensional structures of objects depicted in the plurality of images; and separately merging () the defined relative positions and rotational angles of cameras () for the plurality of sub-models.

The present invention relates to a method for navigating through a set of images depicting a scenery, said method comprising: receiving information relating to a subset of images within said set of images, said information comprising metadata of each image, said subset of images comprising images being acquired from a geographical position, which is within a bounding area surrounding the geographical position of a current image within the set of images; receiving weight information, said weight information controlling weights to be given to a plurality of different measures of similarities based on different types of metadata; and calculating a plurality of combined measures of similarity for pairs of images in the subset of images, said calculating being performed using said metadata and said weight information.

Method for navigating through a set of images

By providing 3D representations of noteworthy locations for comparison with images, the 3D location of the imaging device, as well as the orientation of the device may be determined. The 3D location and orientation of the imaging device then allows for enhanced navigation in a collection of images, as well as enhanced visualization and editing capabilities. The 3D representations of noteworthy locations may be provided in a database that may be stored local or remote to the imaging device or a programmable device processing images obtained from the imaging device.

Automatic detection of noteworthy locations

One embodiment may take the form of a method of operating a computing device in a reduced power state and collecting a first set of data from at least one sensor. Based on the first set of data, the computing device determines a probability that an object is within a threshold distance of the computing device and, if so, the device activates at least one secondary sensor to collect a second set of data. Based on the second set of data, the device determines if the object is a person. If it is a person, a position of the person relative to the computing device is determined and the computing device changes its state based on the position of the person. If the object is not a person, the computing device remains in a reduced power state.

Presence sensing

A method for navigating through a set of images depicting a scene comprises providing a graph representation of correlations of the images and receiving input for navigation that includes an indication of a current image and weight information to control weights to be given to a plurality of measures of similarities of edges in the graph. The indication of the current image is used to determine a current node in the graph representation. At least one best ranked transition image is determined related to the current image by an edge from the current node, said determining comprising using the weight information to weigh the plurality of measures of similarities for forming a combined measure of similarity for edges from the current node, and selecting the at least one best ranked transition image based on a comparison of the combined measure of similarity for the edges from the current node.

Methods for navigating through a set of images

Methods and apparatus for a roof analysis tool for constructing a parameter set, where the parameter set is derived from mapping data for a map region, and where the parameter set describes the roofs for the buildings within the map region. In some cases, the parameter set includes a list of roof type identification values and the respective buildings in the map region for which a given roof type identification value corresponds. The roof analysis tool may operate on a server and work in conjunction with a mobile device, where the mobile device may display map views of a map region such that the map view is based on a three-dimensional model of the map region, and where a portion of the three-dimensional model is based on data generated on the mobile device and a portion of the three-dimensional model is based on data generated on the server.

Identifying and parameterizing roof types in map data

Systems, methods, and computer readable media for adjusting the orientation of an image frame and a scene depicted in the image frame are described. In general, techniques are disclosed for analyzing an image with one or more feature detectors to identify features in the image. An alignment or position associated with one or more features identified in the image may be used to determine a proper orientation for the image frame. The image can then be rotated to the proper orientation. It may also be determined if a scene depicted in the image is properly aligned in the rotated image orientation. If not, alignment information associated with the identified features may be utilized to straighten the depicted scene.

Automatic image orientation and straightening through image analysis

Techniques are provided for efficiently generating 3D information from a set of digital images. Techniques are also provided for displaying groups (or clusters) of digital images using 3D information associated with the digital images. In one technique, a group of digital images are displayed as a stack of thumbnail images where the thumbnail images are aligned on a display with respect to each other based on common features identified in the digital images, camera position, and/or camera pose. In another technique, a group of digital images are organized on a display in either a 3D layout or a 2D layout based on 3D information associated with each digital image in the group. In another technique, a transition effect is generated based on projections of two digital images onto a common scene plane and blending (or cross fading) one of the 3D projections with the other of the 3D projections.

Image group processing and visualization

Techniques are provided to improve the performance and accuracy of landmark point detection using a Constrained Local Model. The accuracy of feature filters used by the model may be improved by supplying positive and negative sets of image data from training image regions of varying shapes and sizes to a linear support vector machine training algorithm. The size and shape of regions within which a feature filter is to be applied may be determined based on a variance in training image data for a landmark point with which the feature filter is associated. A sample image may be normalized and a confidence map generated for each landmark point by applying the feature filters as a convolution on the normalized image. A vector flow map may be pre-computed to improve the efficiency with which a mean landmark point is adjusted toward a corresponding landmark point in a sample image.

Object landmark detection in images

A method, device, system, and computer program for object recognition of a 3D object of a certain object class using a statistical shape model for recovering 3D shapes from a 2D representation of the 3D object and comparing the recovered 3D shape with known 3D to 2D representations of at least one object of the object class.

Growing community of inventors

Bjärred, Sweden

Jan Erik Solem