Abstract: In general, embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing mixed reality processing using at least one of depth-based partitioning of a point cloud capture data object, object-based partitioning of a point cloud capture data object, mapping a partitioned point cloud capture data object to detected objects of a three-dimensional scan data object, performing noise filtering on point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects, and performing geometrically-aware object detection using point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects.

Abstract: In general, this disclosure describes techniques for automatically predicting and visualizing a future development of a skin condition of a patient. In some examples, a computing system is configured to estimate, based on sensor data, a skin-condition type for a skin condition on an affected area of a body of a patient; determine, based on the sensor data and the estimated skin-condition type, modeling data indicative of a typical development of the skin-condition type; generate, based on the sensor data and the modeling data, a 3-dimensional (3-D) model indicative of a predicted future development of the skin condition over time; generate extended reality (XR) imagery of the affected area of the body of the patient overlaid with the 3-D model; and output the XR imagery.

Abstract: In general, embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing mixed reality processing using at least one of depth-based partitioning of a point cloud capture data object, object-based partitioning of a point cloud capture data object, mapping a partitioned point cloud capture data object to detected objects of a three-dimensional scan data object, performing noise filtering on point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects, and performing geometrically-aware object detection using point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects.

Abstract: In general, embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing mixed reality processing using at least one of depth-based partitioning of a point cloud capture data object, object-based partitioning of a point cloud capture data object, mapping a partitioned point cloud capture data object to detected objects of a three-dimensional scan data object, performing noise filtering on point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects, and performing geometrically-aware object detection using point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects.

Abstract: In general, embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing mixed reality processing using at least one of depth-based partitioning of a point cloud capture data object, object-based partitioning of a point cloud capture data object, mapping a partitioned point cloud capture data object to detected objects of a three-dimensional scan data object, performing noise filtering on point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects, and performing geometrically-aware object detection using point cloud capture data objects based at least in part on geometric inferences from three-dimensional scan data objects.

Abstract: Techniques and systems are described to control a determination of document similarity. In one example, dimensionality of the documents is reduced through computation of a signature, e.g., via a hashing technique such as “minhashing” which is also known as min-wise independent permutations locality sensitive hashing. From these signatures, another hashing technique (e.g., locality sensitive hashing) is used to determine similarity of the signatures to each other. Identification of disjoint sets is then used as a basis to partition the documents for determination of document similarity by respective nodes of a plurality of computing devices. In this way, an amount of data shuffling between the nodes as part of the determination of document similarity may be reduced. In another example, a weighting is applied to attributes of documents as part of the determination of document similarity.

Abstract: Techniques and systems are described to control a determination of document similarity. In one example, dimensionality of the documents is reduced through computation of a signature, e.g., via a hashing technique such as “minhashing” which is also known as min-wise independent permutations locality sensitive hashing. From these signatures, another hashing technique (e.g., locality sensitive hashing) is used to determine similarity of the signatures to each other. Identification of disjoint sets is then used as a basis to partition the documents for determination of document similarity by respective nodes of a plurality of computing devices. In this way, an amount of data shuffling between the nodes as part of the determination of document similarity may be reduced. In another example, a weighting is applied to attributes of documents as part of the determination of document similarity.