Abstract: The methods and systems described herein provide for depth-aware image editing and interactive features. In particular, a computer application may provide image-related features that utilize a combination of a (a) the depth map, and (b) segmentation data to process one or more images, and generate an edited version of the one or more images.

Abstract: The methods and systems described herein provide for depth-aware image editing and interactive features. In particular, a computer application may provide image-related features that utilize a combination of a (a) the depth map, and (b) segmentation data to process one or more images, and generate an edited version of the one or more images.

Abstract: The methods and systems described herein provide for depth-aware image editing and interactive features. In particular, a computer application may provide image-related features that utilize a combination of a (a) the depth map, and (b) segmentation data to process one or more images, and generate an edited version of the one or more images.

Abstract: The methods and systems described herein provide for depth-aware image editing and interactive features. In particular, a computer application may provide image-related features that utilize a combination of a (a) the depth map, and (b) segmentation data to process one or more images, and generate an edited version of the one or more images.

Abstract: The methods and systems described herein provide for depth-aware image editing and interactive features. In particular, a computer application may provide image-related features that utilize a combination of a (a) the depth map, and (b) segmentation data to process one or more images, and generate an edited version of the one or more images.

Abstract: A 3D voxel grid of an environment is generated, with a signed value assigned to each voxel representative of that voxel's distance from a nearest surface. For each subset of the 3D voxel grid comprising a surface, an initial surface mesh is generated. As new depth information is received, an updated surface is generated only for those subsets of the 3D voxel grid that exhibit greater than threshold change in signed values over time.

Abstract: Ambiguous portions of an image which have fewer photons of a reflected light signal detected than required to determine depth can be classified as being dark (i.e., reflecting too few photons to derive depth) and/or far (i.e., beyond a range of a camera) based at least in part on expected depth and reflectivity values. Expected depth and reflectivity values for the ambiguous portions of the image may be determined by analyzing a model of an environment created by previously obtained images and depth and reflectivity values. The expected depth and reflectivity values may be compared to calibrated values for a depth sensing system to classify the ambiguous portions of the image as either dark or far based on the actual photon count detected for the image.

Abstract: A system for determining distances to features in a scene is disclosed. The system includes, among other features, a target portion identifier module, a target surface generator, a reflector selection module, a light transport simulation module, a depth measurement correction generation module, and a distance calculation module. The target portion identifier module is configured to identify a plurality of target portions of the scene. The target surface generator is configured to simulate a plurality of target surfaces. The reflector selection module is configured to select a first plurality of reflector surfaces from the plurality of target surfaces and a second plurality of reflector surfaces from the first plurality of reflector surfaces.

Abstract: Ambiguous portions of an image which have fewer photons of a reflected light signal detected than required to determine depth can be classified as being dark (i.e., reflecting too few photons to derive depth) and/or far (i.e., beyond a range of a camera) based at least in part on expected depth and reflectivity values. Expected depth and reflectivity values for the ambiguous portions of the image may be determined by analyzing a model of an environment created by previously obtained images and depth and reflectivity values. The expected depth and reflectivity values may be compared to calibrated values for a depth sensing system to classify the ambiguous portions of the image as either dark or far based on the actual photon count detected for the image.

Abstract: Examples are disclosed for controlling virtual object placement and movement in a physical space as viewed on or through a display. An example method includes receiving a count indicating a number of occupancy transitions over time for a plurality of regions in the physical space, displaying a virtual object in an initial location selected based on the counts for each of the plurality of regions in the physical space, and determining an updated location of the virtual object in the physical space as viewed on or through the display based at least on the initial location of the virtual object and the counts for each of the plurality regions in the physical space. The example method further includes displaying the virtual object in the updated location, the virtual object being controlled to move in the physical space based at least on movements of physical objects through the physical space.

Abstract: Examples are disclosed for controlling virtual object placement and movement in a physical space as viewed on or through a display. An example method includes receiving a count indicating a number of occupancy transitions over time for a plurality of regions in the physical space, displaying a virtual object in an initial location selected based on the counts for each of the plurality of regions in the physical space, and determining an updated location of the virtual object in the physical space as viewed on or through the display based at least on the initial location of the virtual object and the counts for each of the plurality regions in the physical space. The example method further includes displaying the virtual object in the updated location, the virtual object being controlled to move in the physical space based at least on movements of physical objects through the physical space.

Abstract: Examples are disclosed herein that relate to classifying ambiguous data in an image. One example provides, on a computing device, a method of processing image data, the method comprising defining a contiguous area of ambiguous data in an image, determining a border surrounding the contiguous area, classifying the contiguous area as dark or far based on a characteristic of one or more pixels of the border, and for the classified contiguous area, processing one or more pixels of the contiguous area differently if the contiguous area is classified as far than if the contiguous area is classified as dark.

Abstract: An environment includes different objects that are each made up of one or more 3-dimensional volumes. These volumes can overlap one another, resulting in situations in which a particular volume that is overlapped is redundant and can be removed from the set of volumes describing the environment. A two-phase approach is applied in determining whether a particular volume is redundant. In the first phase, a candidate list of source volumes is quickly generated with a small amount of computational effort. In the second phase, the source volumes on the candidate list are analyzed to determine whether the particular volume is fully overlapped by one or a combination of the source volumes.

Abstract: A 3D voxel grid of an environment is generated, with a signed value assigned to each voxel representative of that voxel's distance from a nearest surface. For each subset of the 3D voxel grid comprising a surface, an initial surface mesh is generated. As new depth information is received, an updated surface is generated only for those subsets of the 3D voxel grid that exhibit greater than threshold change in signed values over time.

Abstract: Examples are disclosed herein that relate to classifying ambiguous data in an image. One example provides, on a computing device, a method of processing image data, the method comprising defining a contiguous area of ambiguous data in an image, determining a border surrounding the contiguous area, classifying the contiguous area as dark or far based on a characteristic of one or more pixels of the border, and for the classified contiguous area, processing one or more pixels of the contiguous area differently if the contiguous area is classified as far than if the contiguous area is classified as dark.

Abstract: An environment includes different objects that are each made up of one or more 3-dimensional volumes. These volumes can overlap one another, resulting in situations in which a particular volume that is overlapped is redundant and can be removed from the set of volumes describing the environment. A two-phase approach is applied in determining whether a particular volume is redundant. In the first phase, a candidate list of source volumes is quickly generated with a small amount of computational effort. In the second phase, the source volumes on the candidate list are analyzed to determine whether the particular volume is fully overlapped by one or a combination of the source volumes.