Abstract: Systems, methods, and computer-readable storage media are provided for aligning three-dimensional point clouds that each includes data representing at least a portion of an area-of-interest. The area-of-interest is divided into multiple regions, each region having a closed-loop structure defined by a plurality of border segments, each border segment including a plurality of fragments. Point clouds representing the fragments that make up each closed-loop region are aligned with one another in a parallelized manner, for instance, utilizing a Simultaneous Generalized Iterative Closest Point (SGICP) technique, to create aligned point cloud regions. Aligned point cloud regions sharing a common border segment portion are aligned with one another to create a single, consistent, aligned point cloud having data that accurately represents the area-of-interest.

Abstract: Among other things, one or more techniques and/or systems are provided for quantifying blur of an image. Blur may result due to motion of a camera while the image is captured. Accordingly, motion measurement data corresponding to motion of the camera during an exposure event may be used to create a camera rotation matrix. A camera intrinsic matrix may be obtained based upon a focal length and principle point of the camera. A transformation matrix may be estimated based upon the camera rotation matrix and/or the camera intrinsic matrix. The transformation matrix may be applied to pixels within the image to determine a blur metric for the image. In this way, blur of an image may be quantified offline and/or in real-time during operation of the camera (e.g., so that the image may be re-acquired (e.g., on the fly) if the image is regarded as being overly blurry).

Abstract: Among other things, one or more techniques and/or systems are provided for classifying a water-body. For example, initial water-body segmentation may be used to segment imagery into water-body features or non-water-body features to create an initial water-body map. The initial water-body map may be refined based upon confidence scores assigned to pixels within the imagery. In one example, a confidence score may correspond to a confidence that a stereo matching technique produced a correct elevation for a pixel. A relatively low confidence score may indicate that the pixel corresponds to water (e.g., due to a lack of features/texture on water), while a relatively high confidence score may indicate that the pixel does not correspond to water (e.g., due to presence of features/texture, such as roads, building corners, etc.). In this way, confidence scores may, for example, be used to refine the initial water-body map to create a final water-body map.

Abstract: Among other things, one or more techniques and/or systems are provided for quantifying blur of an image. Blur may result due to motion of a camera while the image is captured. Accordingly, motion measurement data corresponding to motion of the camera during an exposure event may be used to create a camera rotation matrix. A camera intrinsic matrix may be obtained based upon a focal length and principle point of the camera. A transformation matrix may be estimated based upon the camera rotation matrix and/or the camera intrinsic matrix. The transformation matrix may be applied to pixels within the image to determine a blur metric for the image. In this way, blur of an image may be quantified offline and/or in real-time during operation of the camera (e.g., so that the image may be re-acquired (e.g., on the fly) if the image is regarded as being overly blurry).