In a general aspect, enhancement of artificial intelligence algorithms using 3D data is described. In some aspects, input data of an object is stored in a storage engine of a system. The input data includes first-order primitives and second-order primitives. A plurality of features of the object is determined by operation of an analytics engine of the system, based on the first-order primitives and the second-order primitives. A tensor field is generated by operation of the analytics engine of the system. The tensor field includes an attribute set, which includes one or more attributes selected from the first-order primitives, the second-order primitives, or the plurality of features. The tensor field is processed by operation of the analytics engine of the system according to a series of artificial intelligence algorithms to generate output data representing the object.

Systems and methods for semantic labeling of point clouds using images. Some implementations may include obtaining a point cloud that is based on lidar data reflecting one or more objects in a space; obtaining an image that includes a view of at least one of the one or more objects in the space; determining a projection of points from the point cloud onto the image; generating, using the projection, an augmented image that includes one or more channels of data from the point cloud and one or more channels of data from the image; inputting the augmented image to a two dimensional convolutional neural network to obtain a semantic labeled image wherein elements of the semantic labeled image include respective predictions; and mapping, by reversing the projection, predictions of the semantic labeled image to respective points of the point cloud to obtain a semantic labeled point cloud.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel processing of video frames using neural networks. One of the methods includes receiving a video sequence comprising a respective video frame at each of a plurality of time steps; and processing the video sequence using a video processing neural network to generate a video processing output for the video sequence, wherein the video processing neural network includes a sequence of network components, wherein the network components comprise a plurality of layer blocks each comprising one or more neural network layers, wherein each component is active for a respective subset of the plurality of time steps, and wherein each layer block is configured to, at each time step at which the layer block is active, receive an input generated at a previous time step and to process the input to generate a block output.

Technologies for time-delayed augmented reality (AR) presentations includes determining a location of a plurality of user AR systems located within the presentation site and determining a time delay of an AR sensory stimulus event of an AR presentation to be presented in the presentation site for each user AR system based on the location of the corresponding user AR system within the presentation site. The AR sensory stimulus event is presented to each user AR system based on the determined time delay associated with the corresponding user AR system. Each user AR system generates the AR sensory stimulus event based on a timing parameter that defines the time delay for the corresponding user AR system such that the generation of the AR sensory stimulus event is time-delayed based on the location of the user AR system within the presentation site.

Data transformation caching in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to a dataset; generating, in dependence upon the one or more transformations, a transformed dataset; storing, within one or more of the storage systems, the transformed dataset; receiving a plurality of requests to transmit the transformed dataset to one or more of the GPU servers; and responsive to each request, transmitting, from the one or more storage systems to the one or more GPU servers without re-performing the one or more transformations on the dataset, the transformed dataset.

An image capturing method and a terminal device are provided. The method includes entering a camera application to start a lens and display a viewfinder interface, converting an original image captured by the lens into a red-green-blue (RGB) image, and decreasing luminance of the RGB image to be less than first luminance or increasing the luminance of the RGB image to be greater than second luminance, to obtain a first image; converting the RGB image into N frames of high-dynamic-range (HDR) images, and fusing color information of pixels in any same location on the first image and the N frames of HDR images to obtain a final image.

In various embodiments, computerized methods and systems for virtualizing electronic devices for utilization in virtual environments are provided. An electronic device located within the vicinity is detected and identified. A virtual object that corresponds to the identified electronic device is obtained for rendering within an immersive virtual environment. The virtual object can include an interactive portion that, when rendered, can provide an interactive virtual experience with what appears to be a virtualized instance of the identified electronic device. The interactive portion of the virtual object is bound to a corresponding interactive portion of the identified electronic device, so that interaction data is properly communicated and interpreted therebetween. The virtual object, having its interactive portion bound to the interactive portion of the identified electronic device, is rendered in virtual space, such that virtual interactions therewith facilitate real interactions with the identified electronic device.

A method is described that includes executing a convolutional neural network layer on an image processor having an array of execution lanes and a two-dimensional shift register. The executing of the convolutional neural network includes loading a plane of image data of a three-dimensional block of image data into the two-dimensional shift register. The executing of the convolutional neural network also includes performing a two-dimensional convolution of the plane of image data with an array of coefficient values by sequentially: concurrently multiplying within the execution lanes respective pixel and coefficient values to produce an array of partial products; concurrently summing within the execution lanes the partial products with respective accumulations of partial products being kept within the two dimensional register for different stencils within the image data; and, effecting alignment of values for the two-dimensional convolution within the execution lanes by shifting content within the two-dimensional shift register array.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

An electronic device includes a sensor module, a dual camera including a first image sensor and a second image sensor, and a controller that processes first image data and second image data. The controller allows at least one of the first image sensor and the second image sensor to maintain a power restricted state based on at least one of a first condition associated with information extracted from the first image data or the second image data, a second condition associated with sensing information collected by the sensor module, and a third condition associated with a zoom characteristic of each of a plurality of lenses, a respective one of the plurality of lenses being mounted in each of the first image sensor and the second image sensor.