Abstract: Embodiments of the present disclosure are directed to a method for generating simulated point cloud data, a device, and a storage medium. The method includes: acquiring at least one frame of point cloud data collected by a road collecting device in an actual environment without a dynamic obstacle as static scene point cloud data; setting, according to set position association information, at least one dynamic obstacle in a coordinate system matching the static scene point cloud data; simulating in the coordinate system, according to the static scene point cloud data, a plurality of simulated scanning lights emitted by a virtual scanner located at an origin of the coordinate system; and updating the static scene point cloud data according to intersections of the plurality of simulated scanning lights and the at least one dynamic obstacle to obtain the simulated point cloud data comprising point cloud data of the dynamic obstacle.

Abstract: A method, an apparatus, a device, and a medium for calibrating a posture of a moving obstacle are provided. The method includes: obtaining a 3D map, the 3D map including first static obstacles; selecting a target frame of data, the target frame of data including second static obstacles and one or more moving obstacles; determining posture information of each of the one or more moving obstacles in a coordinate system of the 3D map; registering the target frame of data with the 3D map; determining posture offset information of the target frame of data in the coordinate system according to a registration result; calibrating the posture information of each of the one or more moving obstacles according to the posture offset information; and adding each of the one or more moving obstacles after the calibrating into the 3D map.

Abstract: The present disclosure provides a method and a device for generating a 3D scene map, a related apparatus and a storage medium. The method includes the following. At least two frames of point cloud data collected by a collection device is obtained. Data registration is performed on the at least two frames of point cloud data. First type of point cloud data corresponding to a movable obstacle is deleted from each frame of point cloud data and each frame of point cloud data is merged to obtain an initial scene map. Second type of point cloud data corresponding to a regularly shaped object is replaced with model data of a geometry model matching with the regularly object for the initial scene map to obtain the 3D scene map.

Abstract: A method includes identifying, by a processor, a first range data set. The first range data set is generated by a first device and includes first range data points. The processor identifies a second range data set. The second range data set is generated by a second device and includes second range data points. The processor compares the second range data points to the first range data points. The processor identifies a third range data set based on the comparison. The third range data set includes a subset of the second range data points. The processor adjusts distance values for first range data points of the first range data set corresponding to a portion of the surface of interest based on the third range data set.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for recognizing a face. A specific embodiment of the method includes: acquiring at least two facial images of a to-be-recognized face under different illuminations using a near-infrared photographing device; generating at least one difference image based on a brightness difference between each two of the at least two facial images; determining a facial contour image of the to-be-recognized, face based on the at least one difference image; inputting the at least two facial images, the at least one difference image, and the facial contour image into a pre-trained real face prediction value calculation model to obtain a real face prediction value of the to-be-recognized face; and outputting prompt information for indicating successful recognition of a real face, in response to determining the obtained real face prediction value being greater than a preset threshold.

Abstract: An approach is provided for acquiring both depth and surface normal of an object using a time-of-flight sensor with multiple distributed light sources. The approach involves causing, at least in part, at least one sequential illumination of at least one object by a plurality of distributed light sources associated with a time-of-flight sensor. The approach also involves causing, at least in part, a capturing of reflected light intensity data using the time-of-flight sensor during the at least one sequential illumination. The approach further involves processing and/or facilitating a processing of the reflected light intensity data to determine at least one depth, at least one surface normal, or a combination thereof of the at least one object.

Abstract: In a method, a first video stream is received from a video camera, and a second video stream is received from a depth camera. A pixel mapping between the video camera and the depth camera is known. The video camera has an update rate greater than that of the depth camera. Optical flow in successive frames of the first video stream is measured, and a portion of the optical flow attributable to depth change is extracted. A scaling factor is calculated for each pixel in successive frames of the first video stream to determine whether a depth change has occurred. A perspective depth correction is applied to each pixel having a depth change. The perspective depth correction is based upon the depth of the corresponding pixel in the most recent frame from the second video stream. A combined video stream having an update rate of the video camera and depth information from the depth camera is obtained.

Abstract: Point cloud data sets representing overlapping physical areas are received. Surface feature points are extracted from the point cloud data sets and coordinates are determined for each point of the point cloud data sets. The point cloud data sets are aligned based on matching surface feature points between the point cloud data sets. Matched surface feature points are moved toward the coordinates of corresponding matched surface feature points and the remaining points are moved based on the moved matched surface feature points and the determined coordinates for each point.

Abstract: The described implementations relate to enhancement images, such as in videoconferencing scenarios. One system includes a poriferous display screen having generally opposing front and back surfaces. This system also includes a camera positioned proximate to the back surface to capture an image through the poriferous display screen.

Abstract: In a method, a first video stream is received from a video camera, and a second video stream is received from a depth camera. A pixel mapping between the video camera and the depth camera is known. The video camera has an update rate greater than that of the depth camera. Optical flow in successive frames of the first video stream is measured, and a portion of the optical flow attributable to depth change is extracted. A scaling factor is calculated for each pixel in successive frames of the first video stream to determine whether a depth change has occurred. A perspective depth correction is applied to each pixel having a depth change. The perspective depth correction is based upon the depth of the corresponding pixel in the most recent frame from the second video stream. A combined video stream having an update rate of the video camera and depth information from the depth camera is obtained.

Abstract: An approach is provided for reconstruction of dynamic arbitrary specular objects. The approach involves determining time-of-flight data for at least one pixel of at least one time-of-flight sensor configured with at least one retro-reflector, wherein the time-of-flight data includes a first distance from the at least one time-of-flight sensor to at least one point of at least one surface, and a second distance from the at least one point to the at least one retro-reflector. The approach also involves determining other time-of-flight data for one or more neighboring pixels which are neighboring the at least one pixel. The approach further involves determining at least one range distance to the at least one point of the at least one surface by causing, at least in part, a factoring out of the second distance from the time-of-flight data by using the other time-of-flight data. The approach also involves causing, at least in part, a reconstruction of the at least one surface using the at least one range distance.

Abstract: An apparatus may be provided including at least one processor and at least one memory including computer program code. The at least one memory and the computer program code may be configured to, with the processor, cause the apparatus to perform various operations. Those operations may include: receiving a plurality of positive sample images comprising images of image capture devices; receiving a captured image captured from an image capture device; determining if the captured image comprises an image of an image capture device; and causing an image provided for presentation on a display to be changed in response to the captured image comprising an image of an image capture device. The apparatus may optionally be caused to analyze the plurality of positive sample images including images of image capture devices to learn to detect images of image capture devices in a captured image.

Abstract: Point cloud data sets representing overlapping physical areas are received. Surface feature points are extracted from the point cloud data sets and coordinates are determined for each point of the point cloud data sets. The point cloud data sets are aligned based on matching surface feature points between the point cloud data sets. Matched surface feature points are moved toward the coordinates of corresponding matched surface feature points and the remaining points are moved based on the moved matched surface feature points and the determined coordinates for each point.

Abstract: An approach is provided for acquiring both depth and surface normal of an object using a time-of-flight sensor with multiple distributed light sources. The approach involves causing, at least in part, at least one sequential illumination of at least one object by a plurality of distributed light sources associated with a time-of-flight sensor. The approach also involves causing, at least in part, a capturing of reflected light intensity data using the time-of-flight sensor during the at least one sequential illumination. The approach further involves processing and/or facilitating a processing of the reflected light intensity data to determine at least one depth, at least one surface normal, or a combination thereof of the at least one object.

Abstract: A method, apparatus and computer program product are provided for registration of interior and exterior three dimensional scans based on a semantic feature of a structure. A method is provided including receiving at least one exterior scan and at least one interior scan of a structure and registering the at least one exterior scan and the at least one interior scan based on a semantic feature of the structure. The sematic feature of the structure is present in the at least one exterior scan and the at least one interior scan.

Abstract: An approach is provided for reconstruction of dynamic arbitrary specular objects. The approach involves determining time-of-flight data for at least one pixel of at least one time-of-flight sensor configured with at least one retro-reflector, wherein the time-of-flight data includes a first distance from the at least one time-of-flight sensor to at least one point of at least one surface, and a second distance from the at least one point to the at least one retro-reflector. The approach also involves determining other time-of-flight data for one or more neighboring pixels which are neighboring the at least one pixel. The approach further involves determining at least one range distance to the at least one point of the at least one surface by causing, at least in part, a factoring out of the second distance from the time-of-flight data by using the other time-of-flight data. The approach also involves causing, at least in part, a reconstruction of the at least one surface using the at least one range distance.

Abstract: A method includes identifying, by a processor, a first range data set. The first range data set is generated by a first device and includes first range data points. The processor identifies a second range data set. The second range data set is generated by a second device and includes second range data points. The processor compares the second range data points to the first range data points. The processor identifies a third range data set based on the comparison. The third range data set includes a subset of the second range data points. The processor adjusts distance values for first range data points of the first range data set corresponding to a portion of the surface of interest based on the third range data set.

Abstract: Estimating a blur kernel distribution for visual iris recognition includes determining a first mathematical relationship between an in-focus position of a camera lens and a distance between the lens and an iris whose image is to be captured by the lens. A second mathematical relationship between the in-focus position of the lens and a standard deviation defining a Gaussian blur kernel distribution is estimated. The first mathematical relationship is used to ascertain a desired focus position of the lens based upon the actual position of the living being's eye at the point in time. The second mathematical relationship is used to calculate a standard deviation defining a Gaussian blur kernel distribution. The produced image is digitally unblurred by using the blur kernel distribution defined by the calculated standard deviation.

Abstract: The described implementations relate to enhancement images, such as in videoconferencing scenarios. One system includes a poriferous display screen having generally opposing front and back surfaces. This system also includes a camera positioned proximate to the back surface to capture an image through the poriferous display screen.

Abstract: The described implementations relate to enhancement images, such as in videoconferencing scenarios. One system includes a poriferous display screen having generally opposing front and back surfaces. This system also includes a camera positioned proximate to the back surface to capture an image through the poriferous display screen.