A Conditional Aggregation 3D Point Cloud ReconstructionMethod Based on Diffusion Model
With the development of computer vision,the use of images for 3D point cloud reconstruction has become a key technology to connect the real physical world and the digital world.Existing research often applies more global im-age features to infer objects with reasonable shapes and appearances,resulting in reconstruction The 3D point cloud has the problem of missing details.In order to solve this problem,based on the diffusion model,aconditional aggregation method is introduced to fuse image information as diffusion conditions,and a conditional diffusion three-dimensional point cloud reconstruction network model(CDPRN)is constructed,and the image feature extraction module is used to capture Image features,fully mining image information,using projection transformation to aggregate point clouds and corresponding local image information,allowing the diffusion model to focus on the reconstruction process of each point in the point cloud,improving the accuracy and detail performance of three-dimensional point cloud reconstruction.Comparison and ablation experiments on the ShapeNet data set show that CDPRN can make full use of image informa-tion,enhance the detail expression of the reconstruction results,and improve reconstruction performance.
diffusion model3D point cloud reconstructionconditional aggregation
万方数据
维普
