Using 2.5D Sketches for 3D Point Cloud Reconstruction from A Single Image
Dongyi Yao, Fengqi Li, Yi Wang, Hong Bing Yang, Xiuyun Li · 2021
3D reconstruction from a single image is a highly uncertain problem. Unknown information such as and depth of the self-occluded part of an object requires strong prior knowledge of the object. In order to make better use of prior information and generate high-quality 3D model, we first propose a 3D point cloud generator to obtain prior knowledge of the 3D point cloud in datasets. We then design a model to recover 3D shape of the object from a 2D image, which first estimates a 2.5D sketch from the input 2D image and then transfer the knowledge obtained in the 3D point cloud domain to the 2D image domain through the 2.5D sketch. Because 2.5D sketches are easier to obtain from 2D images and the information in a 2.5D sketch is more abundant than that in a 2D image Experiments demonstrate that our method is highly competitive to state-of-art works in 3D point cloud reconstruction on both synthetic datasets and real datasets.