Abstract: Various implementations provide a view of a 3D environment including a portal for viewing a stereo item (e.g., a photo or video) positioned a distance behind the portal. One or more visual effects are provided based on texture of one or more portions of the stereo item, e.g., texture at cutoff or visible edges of the stereo item. The effects change the appearance of the stereo item or the portal itself, e.g., improving visual comfort issues by minimizing window violations or otherwise enhancing the viewing experience. Various implementations provide a view of a 3D environment including an immersive view of a stereo item without using portal. Such a visual effect may be provided to partially obscure the surrounding 3D environment.

Abstract: Various implementations disclosed herein include devices, systems, and methods that determine a 3D location of an edge based on image and depth data. This involves determining a 2D location of a line segment corresponding to an edge of an object based on a light-intensity image, determining a 3D location of a plane based on depth values (e.g., based on sampling depth near the edge/on both sides of the edge and fitting a plane to the sampled points), and determining a 3D location of the line segment based on the plane (e.g., by projecting the line segment onto the plane). The devices, systems, and methods may involve classifying an edge as a particular edge type (e.g., fold, cliff, plane) and detecting the edge based on such classification.

Abstract: For a managed network including multiple nodes providing multiple services and executing multiple applications some embodiments provide a method for generating groupings of network addresses associated with different applications or services. The method analyzes network traffic patterns using a probabilistic topic modeling algorithm to generate the groupings of network addresses. In some embodiments, data is collected and analyzed periodically. A network administrator defines the granularity of the time stamps in some embodiments to monitor changes in network traffic patterns over time for each network address or node and/or for the network as a whole. For each network address or node, a probability distribution over the topics at a given time is stored in some embodiments. The stored distributions are then used to determine a divergence over time of the application or service provided by the network address or node. Additionally, the stored distributions can be used to detect anomalous behavior.

Abstract: For a managed network including multiple nodes providing multiple services and executing multiple applications some embodiments provide a method for generating groupings of network addresses associated with different applications or services. The method analyzes network traffic patterns using a probabilistic topic modeling algorithm to generate the groupings of network addresses. Network traffic patterns are related to the different flows in the network. The method analyzes information about the different flows such as some combination of the network addresses in the network that are a source or destination of the flow, the source or destination port, the number of packets in each flow, the number of bytes exchanged during the life of the flow, a start time of a flow, and the duration of the flow. In some embodiments, the information is collected as part of an internet protocol flow information export (IPFIX) operation or a tcpdump operation.

Abstract: For a managed network including multiple nodes providing multiple services and executing multiple applications some embodiments provide a method for generating groupings of network addresses associated with different applications or services. The method analyzes network traffic patterns using a probabilistic topic modeling algorithm to generate the groupings of network addresses. In some embodiments, data is collected and analyzed periodically. A network administrator defines the granularity of the time stamps in some embodiments to monitor changes in network traffic patterns over time for each network address or node and/or for the network as a whole. For each network address or node, a probability distribution over the topics at a given time is stored in some embodiments. The stored distributions are then used to determine a divergence over time of the application or service provided by the network address or node. Additionally, the stored distributions can be used to detect anomalous behavior.