Abstract: Approaches described and suggested herein relate to generating three-dimensional representations of objects to be used to render virtual reality and augmented reality effects on personal devices such as smartphones and personal computers, for example. An initial surface mesh of an object is obtained. A plurality of silhouette masks of the object taken from a plurality of viewpoints is also obtained. A plurality of depth maps are generated from the initial surface mesh. Specifically, the plurality of depth maps are taken from the same plurality of viewpoints from which the silhouette images are taken. A volume including the object is discretized into a plurality of voxels. Each voxel is then determined to be either inside the object or outside of the object based on the silhouette masks and the depth data. A final mesh is then generated from the voxels that are determined to be inside the object.

Abstract: Devices, systems, and methods are provided for real-time object placement guidance in augmented reality experience. An example method may include receiving, by a device having a sensor, an indication of an object to be viewed in an physical environment of the device. The example method may also include determining a 3D model of the physical environment using data of the physical environment captured by the sensor. The example method may also include determining that a first surface in the 3D model of the environment is a first floor space, and a second surface in the 3D model of the environment is a first wall space. The example method may also include determining that a portion of the first surface is unoccupied and sized to fit the object.

Abstract: System and methods are provided that generate a three-dimensional model from a physical space. While a user is scanning and/or recording the physical space with a user computing device, user speech describing the physical space is recorded. A transcript is generated from the audio captured during the scan and/or image recording of the physical space. Keywords from the transcript are used to improve computer-vision object identification, which is incorporated in the three-dimensional model.

Abstract: Devices, systems, and methods are provided for real-time object placement guidance in augmented reality experience. An example method may include receiving, by a device having a sensor, an indication of an object to be viewed in an physical environment of the device. The example method may also include determining a 3D model of the physical environment using data of the physical environment captured by the sensor. The example method may also include determining that a first surface in the 3D model of the environment is a first floor space, and a second surface in the 3D model of the environment is a first wall space. The example method may also include determining that a portion of the first surface is unoccupied and sized to fit the object.

Abstract: Devices, systems, and methods are provided for real-time object placement guidance in augmented reality experience. An example method may include receiving, by a device having a sensor, an indication of an object to be viewed in an physical environment of the device. The example method may also include determining a 3D model of the physical environment using data of the physical environment captured by the sensor. The example method may also include determining that a first surface in the 3D model of the environment is a first floor space, and a second surface in the 3D model of the environment is a first wall space. The example method may also include determining that a portion of the first surface is unoccupied and sized to fit the object.

Abstract: Approaches described and suggested herein relate to generating an enhanced point cloud representation of an objection and generating a surface mesh from the enhanced point cloud. The surface mesh can be used to render three-dimensional representations of objects on personal devices such as smartphones and personal computers, for example. Generating an enhanced point cloud of an object includes capturing a plurality of images of the object from a plurality of viewpoints about the object, generating an initial point cloud representation of the object from the plurality of images, generating a preliminary surface mesh from the point cloud using a Delauney-based meshing algorithm, and sampling points from the preliminary surface mesh. The sampled points are then added to the point cloud to form the enhanced point cloud. A final surface mesh can then be generated from the enhanced point cloud using a Poisson-based meshing algorithm.

Abstract: Approaches in accordance with various embodiments provide for the presentation of augmented reality (AR) content with respect to optically challenging surfaces. Such surfaces can be difficult to locate using conventional optical-based approaches that rely on visible features. Embodiments can utilize the fact that horizontal surfaces can be located relatively easily, and can determine intersections or boundaries of those horizontal surfaces that likely indicate the presence of another surface, such as a vertical wall. This boundary can be determined automatically, through user input, or using a combination of such approaches. Once such an intersection is located, a virtual plane can be determined whose relative location to a device displaying AR content can be tracked and used as a reference for displaying AR content.

Abstract: A machine learning system receives a reference image and generates a series of projected view images of a physical object represented in the images. Parallel neural networks may receive the reference image and series of projected view images for analysis to determine one or more features of the physical object. By pooling the results from the parallel network, a single output may be provided to a set of decodes that are trained to identify a material property of the one or more items. As a result, a three-dimensional model may be generated that includes a graphical representation of the object as a function of its material properties to enable improved rendering.

Abstract: Approaches described and suggested herein relate to generating three-dimensional representations of objects to be used to render virtual reality and augmented reality effects on personal devices such as smartphones and personal computers, for example. An initial surface mesh of an object is obtained. A plurality of silhouette masks of the object taken from a plurality of viewpoints is also obtained. A plurality of depth maps are generated from the initial surface mesh. Specifically, the plurality of depth maps are taken from the same plurality of viewpoints from which the silhouette images are taken. A volume including the object is discretized into a plurality of voxels. Each voxel is then determined to be either inside the object or outside of the object based on the silhouette masks and the depth data. A final mesh is then generated from the voxels that are determined to be inside the object.

Abstract: Approaches described and suggested herein relate to generating three-dimensional representations of objects to be used to render virtual reality and augmented reality effects on personal devices such as smartphones and personal computers, for example. An initial surface mesh of an object is obtained. A plurality of silhouette masks of the object taken from a plurality of viewpoints is also obtained. A plurality of depth maps are generated from the initial surface mesh. Specifically, the plurality of depth maps are taken from the same plurality of viewpoints from which the silhouette images are taken. A volume including the object is discretized into a plurality of voxels. Each voxel is then determined to be either inside the object or outside of the object based on the silhouette masks and the depth data. A final mesh is then generated from the voxels that are determined to be inside the object.

Abstract: A complex three-dimensional virtual representation of an object can be rendered. Virtual images can be captured representing a plurality of views of the complex virtual representation. The virtual images can be converted into binary masks depicting the object pixels and non-object pixels in the virtual images. The binary masks can be used to create a three-dimensional representation of the object having lower complexity than the first three-dimensional virtual representation of the object. In embodiments, the low complexity three-dimensional virtual representation of the object and the virtual images are sent to a mobile device to render a low payload representation of the object on the mobile device.

Abstract: Shape-based search of a collection of content associated with one or more images of inventory items (“inventory images”) is enabled at least in part by associating the collection of content and/or its associated inventory images with representative refinement shapes. Inventory items may be grouped into categories and at least one refinement shape may be created for each of the categories. A refinement-shape hierarchy may be created by arranging the refinement shapes into parent and child refinement shapes. Inventory images may be associated to at least one of the refinement shapes of the refinement-shape hierarchy based at least in part on similarities between the refinement shapes and shapes of the inventory items reflected in the inventory images.

Abstract: Systems and methods are provided for calculating and using histogram descriptors to compare images and to identify visually similar content. According to at least one embodiment, multiple histograms descriptors are calculated for individual images of a collection of content. These histogram descriptors may be used to identify two or more visually similar images in the collection of content. For example, if a user identifies an image of an item of interest, then embodiments search across the collection of content to identify other images of items that are visually similar to the selected item of interest. To do so, embodiments search across the histogram descriptors of the images in the collection of content to identify one or more images that have histograms descriptors that are similar to the histograms descriptors of the image of the selected item of interest.

Abstract: Systems and methods are provided for creating contour images that represent the contour of objects reflected in images, calculating contour histogram descriptors of the contour images, and classifying images based in part on the histogram descriptors of the contour images. For example, a contour image of an image is created. A radial-polar grid having a plurality of radial-polar bins is then positioned on the contour image. A contour histogram descriptor is created to include a number of bins that correspond to the radial-polar bins of the radial-polar grid, where the contents of the bins of the contour histogram descriptor represent the number of pixels of the contour image that are located in the corresponding radial-polar bins of the radial-polar grid. Images are classified at least based in part on comparisons between contour histogram descriptors of the images and contour histogram descriptors of training images.