Abstract: Disclosed are systems and methods for splat filling a three-dimensional (“3D”) model using semi-measured data. The splat filling includes generating measured data points for a 3D representation of a scene with positions that are measured from scanning the scene, and with color values defined from measured color values of used pixels from a two-dimensional (“2D”) of the scene. The splat filling includes generating a semi-measured data point based on an unused pixel of the 2D image. The position of the semi-measured data point is derived based on a separation between the unused pixel and one or more used pixels of the 2D image, and based on the positions of measured data points that are defined with the measured color values of the one or more pixels. The color values of the semi-measured data point are defined directly from the measured color values associated with the unused pixel.

Abstract: A method of detecting objects in three-dimensional (3D) point clouds and detecting differences between 3D point clouds and the objects therein is disclosed. A method includes receiving a first scene 3D point cloud and a second scene 3D point cloud, wherein the first scene 3D point cloud and the second scene 3D point cloud include first and second target objects, respectively; aligning the first scene 3D point cloud and the second scene 3D point cloud; detecting the first and second target objects from the first scene 3D point cloud and the second scene 3D point cloud, respectively; comparing the detected first target object with the detected second target object; and identifying, based on the comparison, one or more differences between the detected first target object and the detected second target object. Further aspects relate to detecting changes of target objects within scenes of multiple 3D point clouds.

Abstract: A method of detecting objects in three-dimensional (3D) point clouds and detecting differences between 3D point clouds and the objects therein is disclosed. A method includes receiving a first scene 3D point cloud and a second scene 3D point cloud, wherein the first scene 3D point cloud and the second scene 3D point cloud include first and second target objects, respectively; aligning the first scene 3D point cloud and the second scene 3D point cloud; detecting the first and second target objects from the first scene 3D point cloud and the second scene 3D point cloud, respectively; comparing the detected first target object with the detected second target object; and identifying, based on the comparison, one or more differences between the detected first target object and the detected second target object. Further aspects relate to detecting changes of target objects within scenes of multiple 3D point clouds.