Abstract: Systems and methods are described for establishing Proof of Location for a computing device (which may serve as Proof of Location for a user of the device). In some examples, establishing Proof of Location involves trackable features findable by the device at the location in question. In some examples, the computing device is configured to generate a point cloud of a trackable feature for comparison to a reference point cloud. Anti-spoofing methods are described for reducing the possibility of accidental or intentional false-positive Proof of Location results.

Abstract: A supervisory computer vision (CV) system may include a secondary CV system running in parallel with a native CV system on a mobile device. The secondary CV system is configured to run less frequently than the native CV system. CV algorithms are then run on these less-frequent sample images, generating information for localizing the device to a reference point cloud (e.g., provided over a network) and for transforming between a local point cloud of the native CV system and the reference point cloud. AR content may then be consistently positioned relative to the convergent CV system's coordinate space and visualized on a display of the mobile device. Various related algorithms facilitate the efficient operation of this system.

Abstract: A supervisory computer vision (CV) system may include a secondary CV system running in parallel with a native CV system on a mobile device. The secondary CV system is configured to run less frequently than the native CV system. CV algorithms are then run on these less-frequent sample images, generating information for localizing the device to a reference point cloud (e.g., provided over a network) and for transforming between a local point cloud of the native CV system and the reference point cloud. AR content may then be consistently positioned relative to the convergent CV system's coordinate space and visualized on a display of the mobile device. Various related algorithms facilitate the efficient operation of this system.

Abstract: A supervisory computer vision (CV) system may include a secondary CV system running in parallel with a native CV system on a mobile device. The secondary CV system is configured to run less frequently than the native CV system. CV algorithms are then run on these less-frequent sample images, generating information for localizing the device to a reference point cloud (e.g., provided over a network) and for transforming between a local point cloud of the native CV system and the reference point cloud. AR content may then be consistently positioned relative to the convergent CV system's coordinate space and visualized on a display of the mobile device. Various related algorithms facilitate the efficient operation of this system.

Abstract: A supervisory computer vision (CV) system may include a secondary CV system running in parallel with a native CV system on a mobile device. The secondary CV system is configured to run less frequently than the native CV system. CV algorithms are then run on these less-frequent sample images, generating information for localizing the device to a reference point cloud (e.g., provided over a network) and for transforming between a local point cloud of the native CV system and the reference point cloud. AR content may then be consistently positioned relative to the convergent CV system's coordinate space and visualized on a display of the mobile device. Various related algorithms facilitate the efficient operation of this system.

Abstract: A supervisory computer vision (CV) system may include a secondary CV system running in parallel with a native CV system on a mobile device. The secondary CV system is configured to run less frequently than the native CV system. CV algorithms are then run on these less-frequent sample images, generating information for localizing the device to a reference point cloud (e.g., provided over a network) and for transforming between a local point cloud of the native CV system and the reference point cloud. AR content may then be consistently positioned relative to the convergent CV system's coordinate space and visualized on a display of the mobile device. Various related algorithms facilitate the efficient operation of this system.

Abstract: A supervisory computer vision (CV) system may include a secondary CV system running in parallel with a native CV system on a mobile device. The secondary CV system is configured to run less frequently than the native CV system. CV algorithms are then run on these less-frequent sample images, generating information for localizing the device to a reference point cloud (e.g., provided over a network) and for transforming between a local point cloud of the native CV system and the reference point cloud. AR content may then be consistently positioned relative to the convergent CV system's coordinate space and visualized on a display of the mobile device. Various related algorithms facilitate the efficient operation of this system.

Abstract: A supervisory computer vision (CV) system may include a secondary CV system running in parallel with a native CV system on a mobile device. The secondary CV system is configured to run less frequently than the native CV system. CV algorithms are then run on these less-frequent sample images, generating information for localizing the device to a reference point cloud (e.g., provided over a network) and for transforming between a local point cloud of the native CV system and the reference point cloud. AR content may then be consistently positioned relative to the convergent CV system's coordinate space and visualized on a display of the mobile device. Various related algorithms facilitate the efficient operation of this system.

Abstract: A supervisory computer vision (CV) system may include a secondary CV system running in parallel with a native CV system on a mobile device. The secondary CV system is configured to run less frequently than the native CV system. CV algorithms are then run on these less-frequent sample images, generating information for localizing the device to a reference point cloud (e.g., provided over a network) and for transforming between a local point cloud of the native CV system and the reference point cloud. AR content may then be consistently positioned relative to the convergent CV system's coordinate space and visualized on a display of the mobile device. Various related algorithms facilitate the efficient operation of this system.