Abstract: A method for reconstructing three-dimensional includes the following operations. At least two frames of first key images for current reconstruction are acquired. A first space surrounding visual cones of the at least two frames of the first key images is determined. The first key images are obtained by photographing a to-be-reconstructed target. A first feature map of the first space is determined based on image information in the several frames of the first key images. The first feature map includes first feature information of voxels in the first space. A first reconstruction result of the current reconstruction is obtained based on the first feature map. A second reconstruction result obtained by previous reconstruction is updated based on the first reconstruction result of the current reconstruction.

Abstract: In an image feature matching method, at least two images to be matched are acquired; a feature representation of each image to be matched is obtained by performing feature extraction on the image to be matched, wherein the feature representation comprises a plurality of first local features; transforming the first local features into first transformation features having a global receptive field of the images to be matched; and a first matching result of the at least two images to be matched is obtained by matching first transformation features in the at least two images to be matched.

Abstract: A method for generating a three-dimensional local human body model are provided in embodiments of the disclosure. The method for generating a three-dimensional local human body model includes: acquiring bone parameter adjustment information of a three-dimensional local human body model; adjusting bone parameters of the three-dimensional local human body model according to the bone parameter adjustment information; and generating the three-dimensional local human body model according to the adjusted bone parameters.

Abstract: A method for aligning coordinate systems includes: in response to that a second terminal triggers alignment of the coordinate systems, obtaining, by the second terminal, map information stored in a cloud or obtaining, by the second terminal, the map information from a first terminal; and transforming, by the second terminal, a second coordinate system of the second terminal into an initial coordinate system corresponding to the map information stored in the cloud or obtained from the first terminal, or transforming, by the second terminal, the initial coordinate system corresponding to the map information stored in the cloud or obtained from the first terminal into the second coordinate system of the second terminal; herein the initial coordinate system is used for defining a positional relationship between the first terminal and the second terminal when the map information is in a sharing state.

Abstract: A method and apparatus for processing a virtual object, a device, and a storage medium are provided. The method for processing a virtual object includes: acquiring first bone parameters and first blend deformation data corresponding to a preset three-dimensional virtual object model comprising a plurality of bones, the first blend deformation data indicating a degree of deformation of the preset three-dimensional virtual object; acquiring second bone parameters corresponding to a target three-dimensional virtual object model, and determining a transformation relationship of the second bone parameters with the first bone parameters; and determining second blend deformation data corresponding to the target three-dimensional virtual object model, according to the transformation relationship of the second bone parameters with the first bone parameters, and the first blend deformation data.

Abstract: A depth recovery method includes: performing feature extraction on a monocular image to obtain a feature image of the monocular image; decoupling the feature image to obtain a scene structure graph of the feature image; performing gradient sensing on the feature image and the scene structure graph to obtain a region-enhanced feature image; and performing depth estimation according to the region-enhanced feature image to obtain a depth image of the monocular image.

Abstract: The present disclosure relates to a key point detection method and apparatus, an electronic device and a storage medium. The method comprises: determining an area in which a plurality of pixels of an image to be processed are located and first direction vectors of the plurality of pixels pointing to a key point of the area; and determining the position of the key point in the area based on the area in which the pixels are located and the first direction vectors of the plurality of pixels in the area.

Abstract: The present disclosure relates to a pose estimation method and device, an electronic apparatus, and a storage medium, the method comprising: performing keypoint detection processing on a target object in an image to be processed to obtain a plurality of keypoints of the target object in the image to be processed and a first covariance matrix corresponding to each keypoint; screening out a target keypoint from the plurality of keypoints in accordance with the first covariance matrix corresponding to each keypoint; and performing pose estimation processing in accordance with the target keypoint to obtain a rotation matrix and a displacement vector.

Abstract: A depth recovery method includes: performing feature extraction on a monocular image to obtain a feature image of the monocular image; decoupling the feature image to obtain a scene structure graph of the feature image; performing gradient sensing on the feature image and the scene structure graph to obtain a region-enhanced feature image; and performing depth estimation according to the region-enhanced feature image to obtain a depth image of the monocular image.