A computer implemented method for reconstructing a three-dimensional urban scene, comprising: acquiring a point cloud data frame set for the three-dimensional scene and a plurality of trajectories via a plurality of collections by a moving laser radar, point cloud data frames in the point cloud data frame set respectively having a pose parameter, the pose parameter comprising coordinates and a pitch angle of the laser radar, determining a trajectory corresponding to the point cloud data frame set based on the pose parameter determined based on a Global Navigation satellite system, GNSS, and/or inertial measurement unit; determining a subset corresponding to a part of the three-dimensional scene from the point cloud data frame set, comprising: determining, based on trajectories corresponding to the point cloud data frame set, a position of an intersection in the three-dimensional scene, wherein the determining a position of an intersection comprising; performing dilation on a trajectory image of the trajectories, to determine a line representing a road by dilating trajectories on the road; for the trajectory image after the dilation, determining a plurality of corner points at the intersection through a corner point detection method; and determining the position of the intersection based on the plurality of corner points at the intersection by clustering; forming, based on the position of the intersection, a connection diagram by connecting the intersection; and determining, based on the connection diagram, a closed path as the part of the three-dimensional scene; adjusting a pose parameter of a point cloud data frame in the subset to obtain an adjusted subset, the adjusted subset including at least two point cloud data frames having matching overlapping parts, the pose parameter being adjusted from an original pose parameter value to an adjusted pose parameter value; and if one point cloud data frame in a point cloud data frame subset corresponds to a GNSS signal of high confidence level, amending the pose parameter of the point cloud data frame adjusted in the preceding step as the original pose parameter value of the point cloud data frame; performing global pose adjustment on the point cloud data frame set using the adjusted subset. acquiring a point cloud data frame set for the three-dimensional scene and a plurality of trajectories via a plurality of collections by a moving laser radar, point cloud data frames in the point cloud data frame set respectively having a pose parameter, the pose parameter comprising coordinates and a pitch angle of the laser radar, determining a trajectory corresponding to the point cloud data frame set based on the pose parameter determined based on a Global Navigation satellite system, GNSS, and/or inertial measurement unit; determining a subset corresponding to a part of the three-dimensional scene from the point cloud data frame set, comprising: determining, based on trajectories corresponding to the point cloud data frame set, a position of an intersection in the three-dimensional scene, wherein the determining a position of an intersection comprising; performing dilation on a trajectory image of the trajectories, to determine a line representing a road by dilating trajectories on the road; for the trajectory image after the dilation, determining a plurality of corner points at the intersection through a corner point detection method; and determining the position of the intersection based on the plurality of corner points at the intersection by clustering; forming, based on the position of the intersection, a connection diagram by connecting the intersection; and determining, based on trajectories corresponding to the point cloud data frame set, a position of an intersection in the three-dimensional scene, wherein the determining a position of an intersection comprising; performing dilation on a trajectory image of the trajectories, to determine a line representing a road by dilating trajectories on the road; for the trajectory image after the dilation, determining a plurality of corner points at the intersection through a corner point detection method; and determining the position of the intersection based on the plurality of corner points at the intersection by clustering; performing dilation on a trajectory image of the trajectories, to determine a line representing a road by dilating trajectories on the road; for the trajectory image after the dilation, determining a plurality of corner points at the intersection through a corner point detection method; and determining the position of the intersection based on the plurality of corner points at the intersection by clustering; forming, based on the position of the intersection, a connection diagram by connecting the intersection; and determining, based on the connection diagram, a closed path as the part of the three-dimensional scene; adjusting a pose parameter of a point cloud data frame in the subset to obtain an adjusted subset, the adjusted subset including at least two point cloud data frames having matching overlapping parts, the pose parameter being adjusted from an original pose parameter value to an adjusted pose parameter value; and if one point cloud data frame in a point cloud data frame subset corresponds to a GNSS signal of high confidence level, amending the pose parameter of the point cloud data frame adjusted in the preceding step as the original pose parameter value of the point cloud data frame; performing global pose adjustment on the point cloud data frame set using the adjusted subset.