Abstract: Method that merges two or more point clouds captured from a scene, eliminates redundant points, and retains points that best represent the scene. The method may generally include a detection step, which locates points from different clouds that are close and thus potentially redundant, followed by a selection step that identifies preferred points. Clouds may be represented as range images, which may simplify both steps. Closeness testing may be optimized by dividing range images into tiles and testing tile bounding volumes for intersections between clouds. Selection of preferred points may incorporate user input, or it may be fully or partially automated. User selection may be performed using 2D drawing tools on range images to identify images with preferred views of a scene. Automated selection may assign a quality measure to points based for example on the surface resolution of each point cloud scan at overlapping points.

Abstract: Enabling colorization and color adjustments on 3D point clouds, which are projected onto a 2D view with an equirectangular projection. A user may color regions on the 2D view and preview the changes immediately in a 3D view of the point cloud. Embodiments render the color of each point in the point cloud by testing whether the 2D projection of the point is inside the colored region. Applications may include generation of a color 3D virtual reality environment using point clouds and color-adjusted imagery.

Abstract: System that enables colorization and color adjustments on 3D point clouds. Point clouds are projected onto a 2D view, for example with an equirectangular projection. A user may color regions on the 2D view, and preview the changes immediately in a 3D view of the point cloud without a time-consuming update of the point cloud data. Because any 2D projection introduces distortions, 3D preview is important for the user to observe the effects of the color changes on the point cloud. Embodiments may for example render the color of each point in the point cloud by testing whether the 2D projection of the point is inside the colored region. Alternatively, the region may be triangulated, and a GPU may render a texture image and use this texture to render the 3D view. Applications may include generation of a color 3D virtual reality environment using point clouds and color-adjusted imagery.

Abstract: Method that merges two or more point clouds captured from a scene, eliminates redundant points, and retains points that best represent the scene. The method may generally include a detection step, which locates points from different clouds that are close and thus potentially redundant, followed by a selection step that identifies preferred points. Clouds may be represented as range images, which may simplify both steps. Closeness testing may be optimized by dividing range images into tiles and testing tile bounding volumes for intersections between clouds. Selection of preferred points may incorporate user input, or it may be fully or partially automated. User selection may be performed using 2D drawing tools on range images to identify images with preferred views of a scene. Automated selection may assign a quality measure to points based for example on the surface resolution of each point cloud scan at overlapping points.

Abstract: Various systems and methods disclosed herein are directed to rendering point-based graphics on a computing device with the spaces between points filled in with color to produce the appearance of surfaces without gaps or holes. According to one method, one or more rasterization passes are performed on an image space. One or more filling passes are performed on the pixels in the image space in which the spaces between the pixels are filled with color to form a contiguous surface in a new image plane. One or more blending passes are performed on the image space after the filling passes, in which wherein the color of a group of pixels is a blended together. A new image space is rendered from the image space in the image buffer.