Abstract: Introduced here computer programs and associated computer-implemented techniques for establishing the dimensions of interior spaces. These computer programs are able to accomplish this by combining knowledge of these interior spaces with spatial information that is output by an augmented reality (AR) framework. Such an approach allows two-dimensional (2D) layouts to be seamlessly created through guided corner-to-corner measurement of interior spaces.

Abstract: Introduced here computer programs and associated computer-implemented techniques for establishing the dimensions of interior spaces. These computer programs are able to accomplish this by combining knowledge of these interior spaces with spatial information that is output by an augmented reality (AR) framework. Such an approach allows two-dimensional (2D) layouts to be seamlessly created through guided corner-to-corner measurement of interior spaces.

Abstract: Introduced here computer programs and associated computer-implemented techniques for generating measurements of physical structures and environments in an automated matter through analysis of data that is generated by one or more sensors included in a computing device. This can be accomplished by combining insights that are derived through analysis different types of data that are generated, computed, or otherwise obtained by a computing device. For instance, a computer program may enable or facilitate measurement of arbitrary dimensions, angles, and square footage of a physical structure based on (i) images generated by an image sensor included in the corresponding computing device and (ii) measurements generated by an inertial sensor included in the corresponding computing device.

Abstract: Introduced here computer programs and associated computer-implemented techniques for generating measurements of physical structures and environments in an automated matter through analysis of data that is generated by one or more sensors included in a computing device. This can be accomplished by combining insights that are derived through analysis different types of data that are generated, computed, or otherwise obtained by a computing device. For instance, a computer program may enable or facilitate measurement of arbitrary dimensions, angles, and square footage of a physical structure based on (i) images generated by an image sensor included in the corresponding computing device and (ii) measurements generated by an inertial sensor included in the corresponding computing device.

Abstract: Introduced here computer programs and associated computer-implemented techniques for establishing the dimensions of interior spaces. These computer programs are able to accomplish this by combining knowledge of these interior spaces with spatial information that is output by an augmented reality (AR) framework. Such an approach allows two-dimensional (2D) layouts to be seamlessly created through guided corner-to-corner measurement of interior spaces.

Abstract: Introduced here computer programs and associated computer-implemented techniques for generating measurements of physical structures and environments in an automated matter through analysis of data that is generated by one or more sensors included in a computing device. This can be accomplished by combining insights that are derived through analysis different types of data that are generated, computed, or otherwise obtained by a computing device. For instance, a computer program may enable or facilitate measurement of arbitrary dimensions, angles, and square footage of a physical structure based on (i) images generated by an image sensor included in the corresponding computing device and (ii) measurements generated by an inertial sensor included in the corresponding computing device.

Abstract: Methods, systems, and devices for generating a training set and using the training set to train an artificial intelligence algorithm to detect and blur faces in images of an interior space are described. An example method for generating a training set includes selecting a first image depicting an interior of a room, selecting a second image depicting at least a face of a person, generating, based on a transparency parameter and a roughness parameter, a semi-transparent surface image, selecting an illumination parameter to configure a first illumination level for the first image and a second illumination level for the second image, and generating a first composite image of the training data set by combining the illumination parameter, the first image, the second image, and the semi-transparent surface image.

Abstract: Introduced here computer programs and associated computer-implemented techniques for establishing the dimensions of interior spaces. These computer programs are able to accomplish this by combining knowledge of these interior spaces with spatial information that is output by an augmented reality (AR) framework. Such an approach allows two-dimensional (2D) layouts to be seamlessly created through guided corner-to-corner measurement of interior spaces.

Abstract: Embodiments of the disclosed technology are directed to using pixel data from two or images of the same real-world region to determine the orientation of substantially planar surfaces in those images. The disclosed technology may be utilized to generate positions and/or orientations of planar surfaces based on multiple 2D images of the planar surface. Implementations can include modeling a planar surface, which can include capturing at least two images (from corresponding different, known positions), determining, from the images, regions that correspond to substantially the same portion of the real-world planar surface, computing a similarity metric for the two regions, and then determining the orientation of the planar surface based on maximizing the similarity metric over various different regions from the images.

Abstract: Systems and methods for creating a two-dimensional orthographic map that models an environment using images captured with a camera and an augmented reality (AR) engine. The interior has at least one object with one or more planes that are parallel to each other. A method can include annotating the at least one image with a plurality of horizontal lines or vertical lines, each horizontal line or vertical line corresponding to an edge of the at least one object in at least one of the one or more planes, determining, an orientation of a projection plane that is parallel to each of the one or more planes, estimating an offset for each of the one or more planes relative to the projection plane, and rendering, based on the offset for each of the one or more planes, a two-dimensional orthographic map that models the interior environment.

Abstract: Systems and methods are disclosed with associated techniques for ensuring that structures are fully imaged, which may permit individuals to utilize mobile computing devices to record imagery of structures for the purpose of documenting their presence, size, location, and the like. As digital images are captured by a computing device, those digital images may be presented in the form of a video feed on a display of the computing device. One or more digital elements can then be overlaid on the video feed. To incentivize a user to move the computing device in a predetermined manner by guided live feedback, the appearance of these digital elements may be manipulated based on movements of the computing device.

Abstract: Methods, systems, and devices for generating a training set and using the training set to train an artificial intelligence algorithm to detect and blur faces in images of an interior space are described. An example method for generating a training set includes selecting a first image depicting an interior of a room, selecting a second image depicting at least a face of a person, generating, based on a transparency parameter and a roughness parameter, a semi-transparent surface image, selecting an illumination parameter to configure a first illumination level for the first image and a second illumination level for the second image, and generating a first composite image of the training data set by combining the illumination parameter, the first image, the second image, and the semi-transparent surface image.

Abstract: Introduced here computer programs and associated computer-implemented techniques for generating measurements of physical structures and environments in an automated matter through analysis of data that is generated by one or more sensors included in a computing device. This can be accomplished by combining insights that are derived through analysis different types of data that are generated, computed, or otherwise obtained by a computing device. For instance, a computer program may enable or facilitate measurement of arbitrary dimensions, angles, and square footage of a physical structure based on (i) images generated by an image sensor included in the corresponding computing device and (ii) measurements generated by an inertial sensor included in the corresponding computing device.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for generating top-down models of interior spaces based on analysis of digital images of those interior spaces. These computer programs may permit individuals to utilize conventional computing devices to record imagery of interior spaces for the purpose of depth mapping (also referred to as “height mapping”). The end result—namely, the top-down models—may be similar to the digital elevation models that are commonly generated to represent elevation.

Abstract: Introduced here computer programs and associated computer-implemented techniques for establishing the dimensions of interior spaces. These computer programs are able to accomplish this by combining knowledge of these interior spaces with spatial information that is output by an augmented reality (AR) framework. Such an approach allows two-dimensional (2D) layouts to be seamlessly created through guided corner-to-corner measurement of interior spaces.

Abstract: Methods, systems, and devices for generating a training set and using the training set to train an artificial intelligence algorithm to detect and blur faces in images of an interior space are described. An example method for generating a training set includes selecting a first image depicting an interior of a room, selecting a second image depicting at least a face of a person, generating, based on a transparency parameter and a roughness parameter, a semi-transparent surface image, selecting an illumination parameter to configure a first illumination level for the first image and a second illumination level for the second image, and generating a first composite image of the training data set by combining the illumination parameter, the first image, the second image, and the semi-transparent surface image.