Abstract: An example system comprises a base with a bottom end and a top end opposite the bottom end, a motor within the base, the motor being coupled to a rotational component of the base and configured to turn the rotational component about an axis of rotation, the rotational component being at the top end of the base, the axis of rotation being perpendicular to the top end of the base, and an arm coupled to the rotational component, the arm configured to move a holding member above the top of the base, the holding member configurable to hold a digital device above the top end of the base, the arm being adjustable to position and tilt the holding member, the rotational component being capable of turning the arm and the holding member, the arm configured to tilt the holding member at a first angle relative to the arm.

Abstract: Systems and techniques for processing and/or transmitting three-dimensional (3D) data are presented. A partitioning component receives captured 3D data associated with a 3D model of an interior environment and partitions the captured 3D data into at least one data chunk associated with at least a first level of detail and a second level of detail. A data component stores 3D data including at least the first level of detail and the second level of detail for the at least one data chunk. An output component transmits a portion of data from the at least one data chunk that is associated with the first level of detail or the second level of detail to a remote client device based on information associated with the first level of detail and the second level of detail.

Abstract: An example method comprises simulating an environment including at least one simulated object, a simulated ray source, and a simulated receiver, simulating a plurality of rays emitting from the simulated ray source, tracking each ray in the environment, detecting changes for at least one ray that interacts with the at least one simulated object, the changes for the at least one ray including a reflection from the at least one object, tracking the reflection of the at least part of the ray from the at least one object in the environment, determining measurements for any of the plurality of rays that interact with the simulated receiver in the simulation, the at least part of the ray being received by the receiver, the measurements including intensity for any of the plurality of rays that interact with the receiver, and generating synthetic point cloud data based on the measurements.

Abstract: A system comprising: processors and memory containing instructions to control processors to: receive images representing an interior of a physical environment, identify, using neural network for object recognition, an object in an image, the object is associated with a location relative to the physical environment, identify, using neural network for object recognition, another object in another image, determine if objects in the images are located near or at a similar location based on location information associated with the objects, if the objects are located near or at a similar location, then objects are an instance of a single object, store similar location associated with the single object, display an interactive walkthrough visualization of a 3D model of the physical environment including the single object, receive request regarding object location through the interactive walkthrough visualization, and provide the similar location of the single object for display in the interactive walkthrough visualiz

Abstract: An environmental capture system (ECS) captures image data and depth information in a 360-degree scene. The captured image data and depth information can be used to generate a 360-degree scene. The ECS comprises a frame, a drive train mounted to the frame, and an image capture device coupled to the drive train to capture, while pointed in a first direction, a plurality of images at different exposures in a first field of view (FOV) of the 360-degree scene. The ECS further comprises a depth information capture device coupled to the drive train. The depth information capture device and the image capture device are rotated by the drive train about a first, substantially vertical, axis from the first direction to a second direction. The depth information capture device, while being rotated from the first direction to the second direction, captures depth information for a first portion of the 360-degree scene.

Abstract: An example system may access data of a multidimensional space representing a physical environment and identify interior elements within the multidimensional space using a first machine learning model. The interior element may represent furniture in the physical environment. The system may mask one or more of the interior elements with masks and fill each of the masks with imagery of the physical environment to create an appearance of a defurnished space, the defurnished space having the one or more interior elements appearing as missing from the multidimensional space representing the physical environment. The system may provide all or some of the defurnished space for display.

Abstract: Systems and methods for building a three-dimensional composite scene are disclosed. Certain embodiments of the systems and methods may include the use of a three-dimensional capture device that captures a plurality of three-dimensional images of an environment. Some embodiments may further include elements concerning aligning and/or mapping the captured images. Various embodiments may further include elements concerning reconstructing the environment from which the images were captured. The methods disclosed herein may be performed by a program embodied on a non-transitory computer-readable storage medium when executed the program is executed a processor.

Abstract: An apparatus comprising a housing, a mount configured to be coupled to a motor to horizontally move the apparatus, a wide-angle lens coupled to the housing, the wide-angle lens being positioned above the mount thereby being along an axis of rotation, the axis of rotation being the axis along which the apparatus rotates, an image capture device within the housing, the image capture device configured to receive two-dimensional images through the wide-angle lens of environment, and a LiDAR device within the housing, the LiDAR device configured to generate depth data based on the environment.

Abstract: An apparatus comprising a housing, a mount configured to be coupled to a motor to horizontally move the apparatus, a wide-angle lens coupled to the housing, the wide-angle lens being positioned above the mount thereby being along an axis of rotation, the axis of rotation being the axis along which the apparatus rotates, an image capture device within the housing, the image capture device configured to receive two-dimensional images through the wide-angle lens of environment, and a LiDAR device within the housing, the LiDAR device configured to generate depth data based on the environment.

Abstract: An example system comprises a base with a bottom end and a top end opposite the bottom end, a motor within the base, the motor being coupled to a rotational component of the base and configured to turn the rotational component about an axis of rotation, the rotational component being at the top end of the base, the axis of rotation being perpendicular to the top end of the base, and an arm coupled to the rotational component, the arm configured to move a holding member above the top of the base, the holding member configurable to hold a digital device above the top end of the base, the arm being adjustable to position and tilt the holding member, the rotational component being capable of turning the arm and the holding member, the arm configured to tilt the holding member at a first angle relative to the arm.

Abstract: An apparatus comprising a housing, a mount configured to be coupled to a motor to horizontally move the apparatus, a wide-angle lens coupled to the housing, the wide-angle lens being positioned above the mount thereby being along an axis of rotation, the axis of rotation being the axis along which the apparatus rotates, an image capture device within the housing, the image capture device configured to receive two-dimensional images through the wide-angle lens of environment, and a LiDAR device within the housing, the LiDAR device configured to generate depth data based on the environment.

Abstract: Techniques are provided for increasing the accuracy of automated classifications produced by a machine learning engine. Specifically, the classification produced by a machine learning engine for one photo-realistic image is adjusted based on the classifications produced by the machine learning engine for other photo-realistic images that correspond to the same portion of a 3D model that has been generated based on the photo-realistic images. Techniques are also provided for using the classifications of the photo-realistic images that were used to create a 3D model to automatically classify portions of the 3D model. The classifications assigned to the various portions of the 3D model in this manner may also be used as a factor for automatically segmenting the 3D model.

Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system comprising a processor, a panoramic image, and employing, by the system, a three-dimensional data from two-dimensional data (3D-from-2D) convolutional neural network model to derive three-dimensional data from the panoramic image, wherein the 3D-from-2D convolutional neural network model employs convolutional layers that wrap around the panoramic image as projected on a two-dimensional plane to facilitate deriving the three-dimensional data.

Abstract: This application generally relates to defining, displaying and interacting with tags in a 3D model. In an embodiment, a method includes generating, by a system including a processor, a three-dimensional model of an environment based on sets of aligned three-dimensional data captured from the environment, and associating tags with defined locations of the three-dimensional model, wherein the tags are respectively represented by tag icons that are spatially aligned with the defined locations of the three-dimensional model as included in different representations of the three-dimensional model rendered via an interface of a device, wherein the different representations correspond to different perspectives of the three-dimensional model, and wherein selection of the tag icons causes the tags respectively associated therewith to be rendered at the device.

Abstract: Techniques are provided for increasing the accuracy of automated classifications produced by a machine learning engine. Specifically, the classification produced by a machine learning engine for one photo-realistic image is adjusted based on the classifications produced by the machine learning engine for other photo-realistic images that correspond to the same portion of a 3D model that has been generated based on the photo-realistic images. Techniques are also provided for using the classifications of the photo-realistic images that were used to create a 3D model to automatically classify portions of the 3D model. The classifications assigned to the various portions of the 3D model in this manner may also be used as a factor for automatically segmenting the 3D model.

Abstract: The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system comprising a processor, a panoramic image, and employing, by the system, a three-dimensional data from two-dimensional data (3D-from-2D) convolutional neural network model to derive three-dimensional data from the panoramic image, wherein the 3D-from-2D convolutional neural network model employs convolutional layers that wrap around the panoramic image as projected on a two-dimensional plane to facilitate deriving the three-dimensional data.

Abstract: An environmental capture system (ECS) captures image data and depth information in a 360-degree scene. The captured image data and depth information can be used to generate a 360-degree scene. The ECS comprises a frame, a drive train mounted to the frame, and an image capture device coupled to the drive train to capture, while pointed in a first direction, a plurality of images at different exposures in a first field of view (FOV) of the 360-degree scene. The ECS further comprises a depth information capture device coupled to the drive train. The depth information capture device and the image capture device are rotated by the drive train about a first, substantially vertical, axis from the first direction to a second direction. The depth information capture device, while being rotated from the first direction to the second direction, captures depth information for a first portion of the 360-degree scene.

Abstract: An apparatus comprising a housing, a mount configured to be coupled to a motor to horizontally move the apparatus, a wide-angle lens coupled to the housing, the wide-angle lens being positioned above the mount thereby being along an axis of rotation, the axis of rotation being the axis along which the apparatus rotates, an image capture device within the housing, the image capture device configured to receive two-dimensional images through the wide-angle lens of environment, and a LiDAR device within the housing, the LiDAR device configured to generate depth data based on the environment.

Abstract: Systems and methods for building a three-dimensional composite scene are disclosed. Certain embodiments of the systems and methods may include the use of a three-dimensional capture device that captures a plurality of three-dimensional images of an environment. Some embodiments may further include elements concerning aligning and/or mapping the captured images. Various embodiments may further include elements concerning reconstructing the environment from which the images were captured. The methods disclosed herein may be performed by a program embodied on a non-transitory computer-readable storage medium when executed the program is executed a processor.

Abstract: Systems and techniques for processing and/or transmitting three-dimensional (3D) data are presented. A partitioning component receives captured 3D data associated with a 3D model of an interior environment and partitions the captured 3D data into at least one data chunk associated with at least a first level of detail and a second level of detail. A data component stores 3D data including at least the first level of detail and the second level of detail for the at least one data chunk. An output component transmits a portion of data from the at least one data chunk that is associated with the first level of detail or the second level of detail to a remote client device based on information associated with the first level of detail and the second level of detail.