Abstract: Examples of the present disclosure relate to generating optimal scanning trajectories for 3D scenes. In an example, a moveable camera may gather information about a scene. During an initial pass, an initial trajectory may be used to gather an initial dataset. In order to generate an optimal trajectory, a reconstruction of the scene may be generated based on the initial data set. Surface points and a camera position graph may be generated based on the reconstruction. A subgradient may be determined, wherein the subgradient provides an additive approximation for the marginal reward associated with each camera position node in the camera position graph. The subgradient may be used to generate an optimal trajectory based on the marginal reward of each camera position node. The optimal trajectory may then be used by to gather additional data, which may be iteratively analyzed and used to further refine and optimize subsequent trajectories.

Abstract: Examples of the present disclosure relate to generating optimal scanning trajectories for 3D scenes. In an example, a moveable camera may gather information about a scene. During an initial pass, an initial trajectory may be used to gather an initial dataset. In order to generate an optimal trajectory, a reconstruction of the scene may be generated based on the initial data set. Surface points and a camera position graph may be generated based on the reconstruction. A subgradient may be determined, wherein the subgradient provides an additive approximation for the marginal reward associated with each camera position node in the camera position graph. The subgradient may be used to generate an optimal trajectory based on the marginal reward of each camera position node. The optimal trajectory may then be used by to gather additional data, which may be iteratively analyzed and used to further refine and optimize subsequent trajectories.

Abstract: Systems and methods for providing efficient entity record conflation are presented. According to aspects of the disclosed subject matter, a first processing phase is made in regard to conflating location data of a corpus of entity records. This first processing phase is conducted in an offline, asynchronous manner to aggregate the entity records of a corpus of entity records into location clusters, each location cluster of entity records considered to correspond to a same structure at a particular geographic location. A second processing phase is conducted in a near real-time manner in regard to conflating received entity records with the entity records of the corpus of entity records. This second processing phase first matches received entity records to a location cluster, and then matches a received entity record to an entity record within the location cluster. Upon matching the received entity record with an entity record in a location cluster, the two entity records are conflated.

Abstract: A client-specific or client-independent polling interval is provided to each client dynamically based on server load. The polling interval can be provided in the server polling response sent in response to a client polling request. The polling interval can be determined using a feedback control system or using a Bucket Reservation Method. The server uses a next polling interval and a flag that indicates if the previous polling request was ignored. Using these two parameters the server can continuously control the polling frequency from the client to achieve optimal performance.

Abstract: A client-specific or client-independent polling interval is provided to each client dynamically based on server load. The polling interval can be provided in the server polling response sent in response to a client polling request. The polling interval can be determined using a feedback control system or using a Bucket Reservation Method. The server uses a next polling interval and a flag that indicates if the previous polling request was ignored. Using these two parameters the server can continuously control the polling frequency from the client to achieve optimal performance.

Abstract: A client-specific or client-independent polling interval is provided to each client dynamically based on server load. The polling interval can be provided in the server polling response sent in response to a client polling request. The polling interval can be determined using a feedback control system or using a Bucket Reservation Method. The server uses a next polling interval and a flag that indicates if the previous polling request was ignored. Using these two parameters the server can continuously control the polling frequency from the client to achieve optimal performance.