| Summary: | Most of the 3D point cloud generation methods from a single image are only applicable to a single target. However, the real image often contains multiple targets, which is difficult to generate 3D point clouds from the network directly. To address this problem, this work proposes an end-to-end point cloud generation network that integrates three parts: image cropping, image retrieval and point cloud reconstruction. The addition of the image cropping module allows network to deal with multi-target situations that appear in image. The image retrieval module can retrieve similar images in the ShapeNet dataset, and input their corresponding ground truth point cloud and target images to the reconstruction network to obtain the three-dimensional reconstruction model. Experiments on a single image containing single and multiple targets show that our method achieves point cloud reconstruction of real image and obtains a more accurate three-dimensional model.
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