Abstract: A track disassembling device comprises a protective housing, a cleaning component and a lifting pin, wherein the protective housing comprises a shell and a supporting plate, the cleaning component is movably sleeved with the protective housing and comprises a cleaning disc, a positioning rod, a connecting disc, a limit disc, a first spring and a first movable block, and the lifting pin is propped against the cleaning disc. A crank structure is formed by fitting with a connecting rod, a locating pin, a second movable block and other structures through the rotation of a turntable, and the second movable block can make reciprocating movement under the limit of the supporting plate to the second movable block; the second movable block making reciprocating movement can exert pressure on the first fixing component to form an impact when the electric telescopic rod extends, achieving separation from the axis pin.

Abstract: A method for identifying human poses in an image, a computer system, and a non-transitory computer-readable medium are provided. The method includes obtaining the image, encoding the image into a feature map, converting the feature map into a plurality of TPDF maps corresponding to a plurality of predefined part types, and generating a global pose map based on the feature map and the plurality of TPDF maps corresponding to the predefined part types. Each TPDF map corresponds to a respective predefined part type and represents a plurality of vectors connecting each position on the image to a closest body part among the one or more body parts of the respective predefined part type. A global pose map is generated based on the feature map and the plurality of TPDF maps, identifying one or more human bodies having a plurality of body parts in a scene of the image.

Abstract: A computer system obtains an image of a scene captured by a camera and identifies a two-dimensional (2D) pose of the person in the image. The 2D pose includes a plurality of 2D joint positions in the image. The 2D pose is converted to a three-dimensional (3D) pose of the person including a plurality of 3D joint positions. The computer system determines a rotation angle of each joint relative to a T-pose of the person based on the plurality of 3D joint positions. The rotation angle of each joint is applied to a skeleton template of the avatar. The computer system renders the skeleton template of the avatar having the rotation angle for each joint.

Abstract: Embodiments of the present disclosure provide an information processing method and device, the method includes: in response to detecting first change information that a three dimensional map is changed, determining second change information of an anchor point based on the first change information, the anchor point being pre-associated with a first image frame of the three dimensional map; adjusting a pose of the anchor point based on the second change information. According to the method in the present disclosure, when the three dimensional map is changed, the anchor point of the three dimensional map can change with the change of the first image frame with which the anchor point is associated, so that the display of the anchor point remains relatively stable, and the problem that the display of the anchor point is stagger when the three dimensional map is changed is avoided.

Abstract: A method for human body model reconstruction and a reconstruction system are disclosed. The method includes acquiring a target image, and acquiring a segmented image by segmenting the target image based on an object to be reconstructed in the target image, the target image being one front image of the object; acquiring an initial estimate shape and a part of texture information of the object respectively, according to the segmented image; determining an initial 3D model of the object through the initial estimate shape, the initial 3D model being a 3D model without texture; acquiring complete texture information of the object according to the part of texture information and a texture generation model; and generating a 3D reconstruction model of the object based on the initial 3D model and the complete texture information, the 3D reconstruction model being a 3D model with texture.

Abstract: An exemplary method of detecting a light source using an electronic device having a camera and a sensor may include: scanning a real environment using the camera to establish an environment map of the real environment; capturing, using the camera, a first image of a real light source from a first location in the real environment and a second image of the real light source from a second location in the real environment; tracking, using the sensor, a first position and a first orientation of the camera in the environment map while the first image is captured, and a second position and a second orientation of the camera in the environment map while the second image is captured; and computing a position of the real light source in the environment map based on the first position, the first orientation, the second position, and the second orientation.

Abstract: Techniques for rendering a 3D virtual object in an augmented-reality system are described. A system, a method, and a non-transitory memory device for augmented reality rendering of three-dimensional, virtual objects are described. In an example, a number of images of an environment are acquired; relative movement of a camera acquiring the number of images is tracked; camera pose is determined relative to the environment using the number of images and tracked relative movement of the camera; depth and normal surfaces of objects in the environment are estimated using a depth map and a normal map; a surface geometry of the environment is reconstructed using the depth map and the normal map; and the virtual object is rendered using the surface geometry of the environment.

Abstract: An exemplary method of detecting a light source using an electronic device having a camera and a sensor may include: scanning a real environment using the camera to establish an environment map of the real environment; capturing, using the camera, a first image of a real light source from a first location in the real environment and a second image of the real light source from a second location in the real environment; tracking, using the sensor, a first position and a first orientation of the camera in the environment map while the first image is captured, and a second position and a second orientation of the camera in the environment map while the second image is captured; and computing a position of the real light source in the environment map based on the first position, the first orientation, the second position, and the second orientation.

Abstract: Techniques for rendering a 3D virtual object in an augmented-reality system are described. A system, a method, and a non-transitory memory device for augmented reality rendering of three-dimensional, virtual objects are described. In an example, a number of images of an environment are acquired; relative movement of a camera acquiring the number of images is tracked; camera pose is determined relative to the environment using the number of images and tracked relative movement of the camera; depth and normal surfaces of objects in the environment are estimated using a depth map and a normal map; a surface geometry of the environment is reconstructed using the depth map and the normal map; and the virtual object is rendered using the surface geometry of the environment.

Abstract: Techniques for occlusion and collision detection in an AR session are described. In an example, a depth sensor is used to generate a depth image. Distortions in the depth image are reduced or eliminated by at least dividing the depth image into depth layers and moving depth pixels between the layers. An RGBD image is generated from the depth image, as updated, and an RGB image generated at substantially the same time as the depth image. Occlusion of a virtual object is detected based on the RGBD image. Further, a 3D model of the real-world environment is generated from the depth images, as updated, and includes multi-level voxels. Collision with the virtual object is detected based on the multi-level voxels. Rendering of the virtual object in an AR session is based on the occlusion and collision detection.

Abstract: A method for human body model reconstruction and a reconstruction system are disclosed. The method includes acquiring a target image, and acquiring a segmented image by segmenting the target image based on an object to be reconstructed in the target image, the target image being one front image of the object; acquiring an initial estimate shape and a part of texture information of the object respectively, according to the segmented image; determining an initial 3D model of the object through the initial estimate shape, the initial 3D model being a 3D model without texture; acquiring complete texture information of the object according to the part of texture information and a texture generation model; and generating a 3D reconstruction model of the object based on the initial 3D model and the complete texture information, the 3D reconstruction model being a 3D model with texture.

Abstract: A method performed by an electronic device is described. The method includes receiving a set of images. The method also includes determining a motion region and a static region based on the set of images. The method further includes extracting, at a first rate, first features from the motion region. The method additionally includes extracting, at a second rate that is different from the first rate, second features from the static region. The method also includes caching the second features. The method further includes detecting at least one object based on at least a portion of the first features.

Abstract: Provided are systems, methods, and computer-readable medium for operating a neural network. In various implementations, the neural network can receive an input image that includes an object to be identified. The neural network can generate a plurality of initial feature maps using a convolution layers, wherein a first initial feature maps is generated using the input image. The neural network can generate an up-sampled feature map using a de-convolution layer that takes an initial feature map as input, where the up-sampled feature map has a same resolution as the previous initial feature map. The neural network can combine the up-sampled feature map and the previous initial feature map, and use the combined feature map to more accurate identify the object.

Abstract: A method performed by an electronic device is described. The method includes receiving a set of images. The method also includes determining a motion region and a static region based on the set of images. The method further includes extracting, at a first rate, first features from the motion region. The method additionally includes extracting, at a second rate that is different from the first rate, second features from the static region. The method also includes caching the second features. The method further includes detecting at least one object based on at least a portion of the first features.

Abstract: Provided are systems, methods, and computer-readable medium for operating a neural network. In various implementations, the neural network can receive an input image that includes an object to be identified. The neural network can generate a plurality of initial feature maps using a convolution layers, wherein a first initial feature maps is generated using the input image. The neural network can generate an up-sampled feature map using a de-convolution layer that takes an initial feature map as input, where the up-sampled feature map has a same resolution as the previous initial feature map. The neural network can combine the up-sampled feature map and the previous initial feature map, and use the combined feature map to more accurate identify the object.

Abstract: A composition for forming a hydrophilic coating includes an organic binder having an average particle size from about 1 to about 100 nm and an inorganic binder having an average particle size from about 1 to about 40 nm. The composition further includes an inorganic photocatalyst that catalyzes an oxidation-reduction reaction. Characteristically, the photocatalyst includes a photocatalytic metal oxide having an average particle size from about 1 to about 50 nm. Advantageously, coatings formed from the composition have improved stain resistance, low contact angle, and resistance to dirt adherence, while maintaining a clear optical character.

Abstract: A method of digital image stabilization is described. The method includes performing feature-based digital image stabilization (DIS) on an image. The method also includes using output of a global motion detector to correct the feature-based DIS on the image. Using output of the global motion detector may include projecting the image on horizontal slices and vertical slices to create blocks. Motion vectors of each block in the image may be calculated. If at least one block motion vector is determined to be a valid block motion vector, then a global motion vector may be determined from all valid block motion vectors.

Abstract: A system and method of object detection are disclosed. In a particular implementation, a method of processing an image includes receiving, at a processor, image data associated with an image of a scene. The scene includes a road region. The method further includes detecting the road region based on the image data and determining a subset of the image data. The subset excludes at least a portion of the image data corresponding to the road region. The method further includes performing an object detection operation on the subset of the image data to detect an object. The object detection operation performed on the subset of the image data is exclusive of the at least a portion of the image data corresponding to the road region.

Abstract: A system and method of object detection are disclosed. In a particular implementation, a method of processing an image includes receiving, at a processor, image data associated with an image of a scene. The scene includes a road region. The method further includes detecting the road region based on the image data and determining a subset of the image data. The subset excludes at least a portion of the image data corresponding to the road region. The method further includes performing an object detection operation on the subset of the image data to detect an object. The object detection operation performed on the subset of the image data is exclusive of the at least a portion of the image data corresponding to the road region.