A game scoring camera system is disclosed for capturing images of game animals for the purpose of scoring the antlers using an accepted scoring method. One or more cameras are used in a multiscopic arrangement for capturing two-dimensional (2-D) images which are then converted to three-dimensional (3-D) data models, the resulting 3-D data models being used for determining measurements of various antler structures for calculating a score for the set of antlers captured in the images, the score being based on existing antler scoring systems. Some embodiments include one or more cameras, each being mounted on an unmanned aerial vehicle or drone, for capturing images during an aerial survey of game animals located within a particular area. Other embodiments include at least two cameras mounted in a stationary configuration for capturing images of game animals located within a particular area.

Disclosed in the embodiments of the present disclosure are a method for planning three-dimensional scanning viewpoint, a device for planning three-dimensional scanning viewpoint and a computer readable storage medium. After a low-precision digitalized model of an object to be scanned is acquired, viewpoint planning calculation is performed, on the basis of a viewpoint planning algorithm, on point cloud data in the low-precision digitalized model, and then the positions and line-of-sight directions of a plurality of viewpoints in space are calculated when a three-dimensional sensor needs to perform three-dimensional scanning on said object. Calculating viewpoints of a three-dimensional sensor by means of a viewpoint planning algorithm can effectively improve the accuracy and scientific nature of sensor posture determination, greatly improving the efficiency of viewpoint planning, and reducing the time consumed in the whole three-dimensional measurement process.

Disclosed in the embodiments of the present disclosure are a method for planning three-dimensional scanning viewpoint, a device for planning three-dimensional scanning viewpoint and a computer readable storage medium. After a low-precision digitalized model of an object to be scanned is acquired, viewpoint planning calculation is performed, on the basis of a viewpoint planning algorithm, on point cloud data in the low-precision digitalized model, and then the positions and line-of-sight directions of a plurality of viewpoints in space are calculated when a three-dimensional sensor needs to perform three-dimensional scanning on said object. Calculating viewpoints of a three-dimensional sensor by means of a viewpoint planning algorithm can effectively improve the accuracy and scientific nature of sensor posture determination, greatly improving the efficiency of viewpoint planning, and reducing the time consumed in the whole three-dimensional measurement process.

Systems and methods are disclosed for directed image capture of a subject of interest, such as a home. Directed image capture can produce higher quality images such as more centrally located within a display and/or viewfinder of an image capture device, higher quality images have greater value for subsequent uses of captured images such as for information extraction or model reconstruction. Graphical guide(s) facilitate content placement for certain positions and quality assessments for the content of interest can be calculated such as for pixel distance of the content of interest to a centroid of the display or viewfinder, or the effect of obscuring objects. Quality assessments can further include instructions for improving the quality of the image capture for the content of interest.

Systems and methods are disclosed for directed image capture of a subject of interest, such as a home. Directed image capture can produce higher quality images such as more centrally located within a display and/or viewfinder of an image capture device, higher quality images have greater value for subsequent uses of captured images such as for information extraction or model reconstruction. Graphical guide(s) facilitate content placement for certain positions and quality assessments for the content of interest can be calculated such as for pixel distance of the content of interest to a centroid of the display or viewfinder, or the effect of obscuring objects. Quality assessments can further include instructions for improving the quality of the image capture for the content of interest.

A plurality of images may be analyzed to determine an object model. The object model may have a plurality of components, and each of the images may correspond with one or more of the components. Component condition information may be determined for one or more of the components based on the images. The component condition information may indicate damage incurred by the object portion corresponding with the component.

A method for simulating a 3-D image sequence from a sequence of 2-D image frames (110), the method comprising: capturing a plurality of 2-D image frames (110) of a scene from a plurality of different observation points, wherein a first, proximal plane and a second, distal plane is identified within each image frame (110) in the sequence, and wherein each observation point maintains substantially the same first, proximal image plane for each image frame; determining a depth estimate for the first, proximal and second, distal plane within each image frame in the sequence; aligning the first, proximal plane of each image frame (110) in the sequence and shifting the second, distal plane of each subsequent image frame (110) in the sequence based on the depth estimate of the second, distal plane for each image frame (110), to produce a modified image frame corresponding to each 2-D image frame; and displaying the modified image frames sequentially. Also disclosed is a system comprising means for carrying out the above method.

A method for simulating a 3-D image sequence from a sequence of 2-D image frames (110), the method comprising: capturing a plurality of 2-D image frames (110) of a scene from a plurality of different observation points, wherein a first, proximal plane and a second, distal plane is identified within each image frame (110) in the sequence, and wherein each observation point maintains substantially the same first, proximal image plane for each image frame; determining a depth estimate for the first, proximal and second, distal plane within each image frame in the sequence; aligning the first, proximal plane of each image frame (110) in the sequence and shifting the second, distal plane of each subsequent image frame (110) in the sequence based on the depth estimate of the second, distal plane for each image frame (110), to produce a modified image frame corresponding to each 2-D image frame; and displaying the modified image frames sequentially. Also disclosed is a system comprising means for carrying out the above method.

A system for measuring total operating deflection shapes of a structure includes one or more imagers, each including two cameras spaced apart from one another and each oriented and positioned to have corresponding fields of view of a different corresponding section of the structure, with the corresponding sections that may include overlap area of the structure within each of the different sections of the structure. Each of the cameras generates a corresponding data stream, which is communicated to a controller, which is configured to measure the response of the structure to an excitation, such as a vibration or an impulse. The system is configured to convert time-domain data from each of the data streams to the frequency-domain data using a Fourier Transform algorithm and stitching the shapes to obtain the total operating deflection shapes of the structure by scaling and stitching together the frequency-domain data.

A vehicle exterior environment recognition apparatus includes a monocular distance calculator, a relaxation distance calculator, and an updated distance calculator. The monocular distance calculator calculates a monocular distance of a three-dimensional object from a luminance image generated by an imaging unit. The relaxation distance calculator calculates a relaxation distance of the three-dimensional object from two luminance images generated by two imaging units based on a degree of image matching between the two luminance images determined using a threshold more lenient than another threshold used to determine the degree of image matching to generate a stereo distance of the three-dimensional object. The updated distance calculator calculates an updated distance of the three-dimensional object by mixing the monocular distance and the relaxation distance at a predetermined ratio.