Dense Point Cloud Reconstruction Based on a Single Image

How to recover high-resolution 3D point clouds from a single image is of great importance in the field of computer vision. However, due to the limited information contained within a single image, reconstructing a dense point cloud of objects from a single image is a highly challenging task. In this paper, we construct a multi-stage dense point cloud reconstruction network that incorporates a coordinate attention mechanism and a point cloud folding operation. The proposed network model comprises two parts: an image-based sparse point cloud generation network and a folding-based dense point cloud generation network. Firstly, we generate a sparse point cloud by extracting the features of the target object from a single image using an image-based sparse point cloud generation network. Then we use the folding-based dense point cloud generation network to density the generated sparse point cloud. Finally, the two stages are combined by deep learning fine-tuning techniques to form an end-to-end dense point cloud reconstruction network that generates a dense point cloud from a single image. By evaluating the synthetic datasets, the proposed method effectively reconstructs the dense point cloud model of the corresponding object and outperforms existing methods in terms of metrics. Meanwhile, our method also performs well on real-world datasets.