2020
DOI: 10.1007/s11042-020-09722-8
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Single image 3D object reconstruction based on deep learning: A review

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Cited by 92 publications
(44 citation statements)
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References 125 publications
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“…In the future, images with textures and backgrounds can be used for rendering to enrich the dataset, which will make the model more robust to 3D object reconstruction from 2D images in real scenes. In addition, there are other methods, such as exploring new algorithms to extract more effective image features, using different training architectures, and supervising methods to optimize [54].…”
Section: Methodsmentioning
confidence: 99%
“…In the future, images with textures and backgrounds can be used for rendering to enrich the dataset, which will make the model more robust to 3D object reconstruction from 2D images in real scenes. In addition, there are other methods, such as exploring new algorithms to extract more effective image features, using different training architectures, and supervising methods to optimize [54].…”
Section: Methodsmentioning
confidence: 99%
“…Ma and Karaman [25] added sparse depth samples from point cloud data to the network, which improved the accuracy by more than 50%. Aside from depth maps, the output of CNNs has variable formats, such as mesh polygon and point clouds, to describe 3D information when using a single image as the input [45]. Fan et al [46] and Xia et al [47] proposed methods that output a group of point coordinates to describe 3D information.…”
Section: Related Workmentioning
confidence: 99%
“…Thus, our technique could be useful for mesh-based reconstruction with a sensor of this kind. Finally, one can rely on the ability of a deep neural network to reconstruct a 3D shape from a single image (or multiple images) [ 30 , 31 ]. In this area, the conclusion is interestingly that a mesh-based method can generate 3D shapes with higher quality than voxel-based and point-based methods.…”
Section: Related Workmentioning
confidence: 99%