Abstract: Disclosed are techniques for enhancing two-dimensional (2D) image capture of subjects (e.g., a physical structure, such as a residential building) to maximize the feature correspondences available for three-dimensional (3D) model reconstruction. More specifically, disclosed is a computer-vision network configured to provide viewfinder interfaces and analyses to guide the improved capture of an intended subject for specified purposes. Additionally, the computer-vision network can be configured to generate a metric representing a quality of feature correspondences between images of a complete set of images used for reconstructing a 3D model of a physical structure. The computer-vision network can also be configured to generate feedback at or before image capture time to guide improvements to the quality of feature correspondences between a pair of images.

Abstract: Disclosed are techniques for enhancing two-dimensional (2D) image capture of subjects (e.g., a physical structure, such as a residential building) to maximize the feature correspondences available for three-dimensional (3D) model reconstruction. More specifically, disclosed is a computer-vision network configured to provide viewfinder interfaces and analyses to guide the improved capture of an intended subject for specified purposes. Additionally, the computer-vision network can be configured to generate a metric representing a quality of feature correspondences between images of a complete set of images used for reconstructing a 3D model of a physical structure. The computer-vision network can also be configured to generate feedback at or before image capture time to guide improvements to the quality of feature correspondences between a pair of images.

Abstract: Disclosed are techniques for generating a photorealistic image by augmenting or compositing at least a portion of a physical structure (e.g., a house) depicted in a two-dimensional (2D) image with synthetic image data. Additionally, disclosed are techniques for augmenting the depicted physical structure and applying a scene effect to the synthetic image data to create a photorealistic effect.

Abstract: Disclosed are techniques for enhancing two-dimensional (2D) image capture of subjects (e.g., a physical structure, such as a residential building) to maximize the feature correspondences available for three-dimensional (3D) model reconstruction. More specifically, disclosed is a computer-vision network configured to provide viewfinder interfaces and analyses to guide the improved capture of an intended subject for specified purposes. Additionally, the computer-vision network can be configured to generate a metric representing a quality of feature correspondences between images of a complete set of images used for reconstructing a 3D model of a physical structure. The computer-vision network can also be configured to generate feedback at or before image capture time to guide improvements to the quality of feature correspondences between a pair of images.

Abstract: Disclosed are techniques for generating a photorealistic image by augmenting or compositing at least a portion of a physical structure (e.g., a house) depicted in a two-dimensional (2D) image with synthetic image data. Additionally, disclosed are techniques for augmenting the depicted physical structure and applying a scene effect to the synthetic image data to create a photorealistic effect.

Abstract: Identifying a pre-existing three-dimensional (3D) model of a target structure includes receiving at least one two-dimensional (2D) image of a target physical structure; generating a predicted 3D model of the target structure based on the at least one 2D image; generating a search descriptor of the predicted 3D model; querying a data structure storing a plurality of pre-existing descriptors, where each pre-existing descriptor characterizes a previously constructed 3D model of an associated physical structure; and identifying at least one previously constructed 3D model that is substantially similar to the predicted 3D model based on a difference between the search descriptor and the plurality of pre-existing descriptors.

Abstract: Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

Abstract: Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

Abstract: Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

Abstract: Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

Abstract: Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

Abstract: A system for facilitating cardiopulmonary resuscitation (CPR) by a CPR performer on a patient includes first, second, and third sensors configured to be positioned at least partially between a hand of the CPR performer and a chest of the patient. The first sensor is configured to measure a depth of compressions provided by the CPR performer on the patient. The second sensor is configured to measure a rate of the compressions. The third sensor is configured to measure a recoil of the compressions. The system also includes a computing system configured to receive data from the first, second, and third sensors. The computing system is also configured to compare the received data to stored data in a library that corresponds to the received data. The computing system is also configured to generate one or more outputs in response to the comparison.

Abstract: A method and system for mapping fluid objects on a substrate using a microscope inspection system that includes a light source, imaging device, stage for moving a substrate disposed on the stage, and a control module. A computer analysis system includes an object identification module that identifies for each of the objects on the substrate, an object position on the substrate including a set of X, Y, and ? coordinates using algorithms, networks, machines and systems including artificial intelligence and image processing algorithms. At least one of the objects is fluid and has shifted from a prior position or deformed from a prior size.

Abstract: Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

Abstract: A method and system for mapping fluid objects on a substrate using a microscope inspection system that includes a light source, imaging device, stage for moving a substrate disposed on the stage, and a control module. A computer analysis system includes an object identification module that identifies for each of the objects on the substrate, an object position on the substrate including a set of X, Y, and ? coordinates using algorithms, networks, machines and systems including artificial intelligence and image processing algorithms. At least one of the objects is fluid and has shifted from a prior position or deformed from a prior size.

Abstract: Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

Abstract: Systems and methods are provided for pitch determination. An example method includes obtaining an image depicting a structure, the image being captured via a user device positioned proximate to the structure. The image is segmented to identify, at least, a roof facet of the structure. An eave vector and a rake vector which are associated with the roof facet are determined. A normal vector of the roof facet is calculated based on the eave vector and the rake vector, and compared to a vector indicating a vertical direction such as gravity. The angle made out by the normal and a gravity vector may be utilized to calculate the pitch of the roof facet.

Abstract: Disclosed are techniques for generating a photorealistic image by augmenting or compositing at least a portion of a physical structure (e.g., a house) depicted in a two-dimensional (2D) image with synthetic image data. Additionally, disclosed are techniques for augmenting the depicted physical structure and applying a scene effect to the synthetic image data to create a photorealistic effect.

Abstract: A method and system for mapping fluid objects on a substrate using a microscope inspection system that includes a light source, imaging device, stage for moving a substrate disposed on the stage, and a control module. A computer analysis system includes an object identification module that identifies for each of the objects on the substrate, an object position on the substrate including a set of X, Y, and ? coordinates using algorithms, networks, machines and systems including artificial intelligence and image processing algorithms. At least one of the objects is fluid and has shifted from a prior position or deformed from a prior size.

Abstract: Disclosed are techniques for generating a photorealistic image by augmenting or compositing at least a portion of a physical structure (e.g., a house) depicted in a two-dimensional (2D) image with synthetic image data. Additionally, disclosed are techniques for augmenting the depicted physical structure using a minimum amount of three-dimensional (3D) geometric data and applying a scene effect to the synthetic image data to create a photorealistic effect.