Abstract: Provided are a three-dimensional reconstruction method, apparatus and system of a dynamic scene, a server and a medium. The method includes: acquiring multiple continuous depth image sequences of the dynamic scene, where the multiple continuous depth image sequences are captured by an array of drones equipped with depth cameras; fusing the multiple continuous depth image sequences to establish a three-dimensional reconstruction model of the dynamic scene; obtaining target observation points of the array of drones through calculation according to the three-dimensional reconstruction model and current poses of the array of drones; and instructing the array of drones to move to the target observation points to capture, and updating the three-dimensional reconstruction model according to multiple continuous depth image sequences captured by the array of drones at the target observation points.

Abstract: An information interaction method and apparatus, an electronic device and a storage medium are provided, the method comprising: displaying a preset first special effect element in a virtual reality space; determining whether a spatial relationship between a target control part controlled by a user and displayed in the virtual reality space and the first special effect element meets a preset condition; and in response to determining that the preset condition is met, displaying a second special effect associated with the first special effect element.

Abstract: The present disclosure provides a material identification method and a device based on laser speckle and modal fusion, an electronic device and a non-transitory computer readable storage medium. The method includes: performing data acquisition on an object by using a structured light camera to obtain a color modal image, a depth modal image and an infrared modal image; preprocessing the color modal image, the depth modal image and the infrared modal image; and inputting the color modal image, the depth modal image and the infrared modal image preprocessed into a preset depth neural network for training, to learn a material characteristic from a speckle structure and a coupling relation between color modal and depth modal, to generate a material classification model for classifying materials, and to generate a material prediction result in testing by the material classification model of the object.

Abstract: The present disclosure provides a material identification method and a device based on laser speckle and modal fusion, an electronic device and a non-transitory computer readable storage medium. The method includes: performing data acquisition on an object by using a structured light camera to obtain a color modal image, a depth modal image and an infrared modal image; preprocessing the color modal image, the depth modal image and the infrared modal image; and inputting the color modal image, the depth modal image and the infrared modal image preprocessed into a preset depth neural network for training, to learn a material characteristic from a speckle structure and a coupling relation between color modal and depth modal, to generate a material classification model for classifying materials, and to generate a material prediction result in testing by the material classification model of the object.

Abstract: Provided are a depth camera based three-dimensional reconstruction method and apparatus, a device and a storage medium. The method includes: acquiring at least two frames of images obtained by capturing a target scenario by a depth camera; determining, according to the at least two frames of images, relative camera poses in response to capturing the target scenario by the depth camera; by adopting a manner of at least two levels of nested screening, determining at least one feature voxel from each frame of image, where each level of screening adopts a respective voxel partitioning rule; fusing and calculating the at least one feature voxel of each frame of image according to a respective relative camera pose of each frame of image to obtain a grid voxel model of the target scenario; and generating an isosurface of the grid voxel model to obtain a three-dimensional reconstruction model of the target scenario.

Abstract: Provided are a three-dimensional reconstruction method, apparatus and system of a dynamic scene, a server and a medium. The method includes: acquiring multiple continuous depth image sequences of the dynamic scene, where the multiple continuous depth image sequences are captured by an array of drones equipped with depth cameras; fusing the multiple continuous depth image sequences to establish a three-dimensional reconstruction model of the dynamic scene; obtaining target observation points of the array of drones through calculation according to the three-dimensional reconstruction model and current poses of the array of drones; and instructing the array of drones to move to the target observation points to capture, and updating the three-dimensional reconstruction model according to multiple continuous depth image sequences captured by the array of drones at the target observation points.

Abstract: Embodiments of the present invention provide an image processing method and apparatus, where the method includes: determining a Gaussian mixture model of a first area in a first image, where the first area belongs to a background area of the first image, and the first image is a first frame of image in a video; determining a distance from a first pixel in a second image to the Gaussian mixture model; and when the distance from the first pixel to the Gaussian mixture model is less than or equal to a first preset threshold, determining that the first pixel belongs to a background area of the second image. In the embodiments of the present invention, an area to which a pixel belongs can be directly determined according to a distance from the pixel to a Gaussian mixture model, so that image foreground segmentation can be completed efficiently.

Abstract: Embodiments of the present invention provide an image processing method and apparatus, where the method includes: determining a Gaussian mixture model of a first area in a first image, where the first area belongs to a background area of the first image, and the first image is a first frame of image in a video; determining a distance from a first pixel in a second image to the Gaussian mixture model; and when the distance from the first pixel to the Gaussian mixture model is less than or equal to a first preset threshold, determining that the first pixel belongs to a background area of the second image. In the embodiments of the present invention, an area to which a pixel belongs can be directly determined according to a distance from the pixel to a Gaussian mixture model, so that image foreground segmentation can be completed efficiently.