Abstract: A method comprising: obtaining a plurality of three-dimensional (3D) point clouds about a plurality of objects of interest, each of said 3D point clouds being labelled to a category of objects of interest; rendering facades for the objects of interests categorized as buildings using an ambient occlusion method, where illumination of the point cloud is calculated based on light coming from a theoretical hemisphere or sphere around the object of interest; and rendering shapes of the objects of interests categorized as non-buildings by fitting predefined templates of street view objects to the point clouds labelled as non-buildings.

Abstract: The invention relates to a method comprising obtaining a desired field of view (FOV) for an image arranged to be captured, determining a required field of view (r_fov) of the FOV for each active camera sensor of a camera array having a field of view (fov), comparing the r_fov and the fov, and determining a bend angle between adjacent camera sensors to achieve the FOV by camera sensors of the camera sensor array, if the r_fov is less than or equal to the fov. The invention further relates to an apparatus, a system and a computer program product that perform the method.

Abstract: A method comprising: obtaining a three-dimensional (3D) point cloud about at least one object of interest; detecting ground and/or building objects from 3D point cloud data using an unsupervised segmentation method; removing the ground and/or building objects from the 3D point cloud data; and detecting one or more vertical objects from the remaining 3D point cloud data using a supervised segmentation method.

Abstract: A method comprising: obtaining a plurality of three-dimensional (3D) point clouds about a plurality of objects of interest, each of said 3D point clouds being labelled to a category of objects of interest; rendering facades for the objects of interests categorized as buildings using an ambient occlusion method, where illumination of the point cloud is calculated based on light coming from a theoretical hemisphere or sphere around the object of interest; and rendering shapes of the objects of interests categorized as non-buildings by fitting predefined templates of street view objects to the point clouds labelled as non-buildings.

Abstract: The invention relates to a method comprising obtaining a desired field of view (FOV) for an image arranged to be captured, determining a required field of view (r_fov) of the FOV for each active camera sensor of a camera array having a field of view (fov), comparing the r_fov and the fov, and determining a bend angle between adjacent camera sensors to achieve the FOV by camera sensors of the camera sensor array, if the r_fov is less than or equal to the fov. The invention further relates to an apparatus, a system and a computer program product that perform the method.

Abstract: A method, apparatus, and computer program product are provided in order to provide directional viewing in panoramic video content. In the context of a method, the method includes determining, using a processor, a baseline instantaneous dominant heading for segments in panoramic video content. The method further includes determining a viewing preference indication and causing a view of the panoramic video content to be provided for display based on the determined viewing preference indication and the baseline instantaneous dominant heading. A corresponding apparatus and a computer program product are also provided.

Abstract: A method comprising: obtaining a three-dimensional (3D) point cloud about at least one object of interest; detecting ground and/or building objects from 3D point cloud data using an unsupervised segmentation method; removing the ground and/or building objects from the 3D point cloud data; and detecting one or more vertical objects from the remaining 3D point cloud data using a supervised segmentation method.

Abstract: A method comprising: obtaining an image about an at least one object of interest and a three-dimensional (3D) point cloud about said object of interest; aligning the 3D point cloud with the image; segmenting the image into a plurality of superpixels preserving a graph structure and spatial neighbourhood of pixel data of the image; associating the superpixels in the image with a subset of said 3D points, said subset of 3D points representing a planar patch in said object of interest; extracting a plurality of 3D features for each patch; and assigning at least one vector representing at least one 3D feature with a semantic label on the basis of at least one extracted 3D feature of the patch.