Training methods, 3D reconstruction methods, devices, storage media, and program products for 3D Gaussian point rendering models.

Abstract

This disclosure relates to a training method, a 3D reconstruction method, an apparatus, a storage medium, and a program product for a 3D Gaussian point rendering model. The method includes: dividing the Gaussian point cloud into multiple Gaussian point clusters based on the positions of Gaussian points in a 3D space corresponding to a target scene; inputting camera parameters of the viewpoint to be predicted into the 3D Gaussian point rendering model corresponding to the target scene, and outputting a rendered image corresponding to the viewpoint to be predicted through the 3D Gaussian point rendering model; calculating the value of a loss function corresponding to the 3D Gaussian point rendering model based on the rendered image and a reference image corresponding to the viewpoint to be predicted; and updating the parameters of the Gaussian points in the Gaussian point cloud based on the value of the loss function, wherein the parameters of Gaussian points in the same Gaussian point cluster are stored in contiguous video memory. This disclosure can significantly optimize video memory access efficiency, reduce fragmented video memory access, and thus reduce video memory pressure.

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