Abstract: Embodiments of the present disclosure disclose a static object reconstruction method and system that are applied to the field of graph and image processing technologies. In the embodiments of the present disclosure, when a static object reconstruction system does not obtain, by means of calculation, an extrinsic camera parameter in a preset time when calculating the extrinsic camera parameter based on a three-dimensional feature point, it indicates that depth data collected by a depth camera is lost or damaged, and a two-dimensional feature point is used to calculate the extrinsic camera parameter to implement alignment of point clouds of a frame of image according to the extrinsic camera parameter. In this way, a two-dimensional feature point and a three-dimensional feature point are mixed, which can implement that a static object can also be successfully reconstructed when depth data collected by a depth camera is lost or damaged.

Abstract: A data processing method and apparatus are provided. The method includes obtaining a first reconstruction model of a target object and dividing the first reconstruction model into M local blocks. Additionally, the method includes obtaining N target object sample alignment models, where each target object sample alignment model and the first reconstruction model have a same corresponding posture parameter, each target object sample alignment model includes M local blocks, and the ith local block of each target object sample alignment model is aligned with the ith local block of the first reconstruction model, where i is 1, . . . , or M. The method also includes approximating the N target object sample alignment models to the first reconstruction model, to determine a second reconstruction model that is of the target object and includes M local blocks.

Abstract: Embodiments of the present disclosure disclose a static object reconstruction method and system that are applied to the field of graph and image processing technologies. In the embodiments of the present disclosure, when a static object reconstruction system does not obtain, by means of calculation, an extrinsic camera parameter in a preset time when calculating the extrinsic camera parameter based on a three-dimensional feature point, it indicates that depth data collected by a depth camera is lost or damaged, and a two-dimensional feature point is used to calculate the extrinsic camera parameter to implement alignment of point clouds of a frame of image according to the extrinsic camera parameter. In this way, a two-dimensional feature point and a three-dimensional feature point are mixed, which can implement that a static object can also be successfully reconstructed when depth data collected by a depth camera is lost or damaged.