Abstract: The disclosed computer-implemented method may include acquiring, from a client device within a real-world environment, information representative of the real-world environment, and transmitting the information representative of the real-world environment to a relocalization service. The method may further include receiving, from the relocalization service, (1) an anchor point that may include a mapped position within the real-world environment, and (2) a determined position within the real-world environment of a client device relative to the mapped position of the anchor point. The method may further include sending an identifier of the anchor point to an asset management service, and obtaining, from the asset management service, a digital asset. The method may further include presenting the digital asset at a position within an artificial environment relative to the mapped position of the anchor point. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include acquiring, from a client device within a real-world environment, information representative of the real-world environment, and transmitting the information representative of the real-world environment to a relocalization service. The method may further include receiving, from the relocalization service, (1) an anchor point that may include a mapped position within the real-world environment, and (2) a determined position within the real-world environment of a client device relative to the mapped position of the anchor point. The method may further include sending an identifier of the anchor point to an asset management service, and obtaining, from the asset management service, a digital asset. The method may further include presenting the digital asset at a position within an artificial environment relative to the mapped position of the anchor point. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include acquiring, from a client device within a real-world environment, information representative of the real-world environment, and transmitting the information representative of the real-world environment to a relocalization service. The method may further include receiving, from the relocalization service, (1) an anchor point that may include a mapped position within the real-world environment, and (2) a determined position within the real-world environment of a client device relative to the mapped position of the anchor point. The method may further include sending an identifier of the anchor point to an asset management service, and obtaining, from the asset management service, a digital asset. The method may further include presenting the digital asset at a position within an artificial environment relative to the mapped position of the anchor point. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed computer-implemented method may include acquiring, from a client device within a real-world environment, information representative of the real-world environment, and transmitting the information representative of the real-world environment to a relocalization service. The method may further include receiving, from the relocalization service, (1) an anchor point that may include a mapped position within the real-world environment, and (2) a determined position within the real-world environment of a client device relative to the mapped position of the anchor point. The method may further include sending an identifier of the anchor point to an asset management service, and obtaining, from the asset management service, a digital asset. The method may further include presenting the digital asset at a position within an artificial environment relative to the mapped position of the anchor point. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A variation of scale-invariant feature transform (SIFT) based on pooling gradient orientations across different domain sizes, in addition to spatial locations. The resulting descriptor is called DSP-SIFT, and it outperforms other methods in wide-baseline matching benchmarks, including those based on convolutional neural networks, despite having the same dimension of SIFT and requiring no training. Problems of local representation of imaging data are also addressed as computation of minimal sufficient statistics that are invariant to nuisance variability induced by viewpoint and illumination. A sampling-based and a point-estimate based approximation of such representations are described.