Implicit surface reconstruction based on object single view
The three-dimensional(3D)reconstruction method based on implicit surface provides a good trade-off in terms of fidelity,flexibility and compression ability.In this paper,the implicit surface network is used to learn the 3D surface of an object shape.Firstly,the global feature is extracted from the image by using visual geometry group-16(VGG-16)network,and the local feature is obtained from the VGG-16 network for each sampling point in the modeling space.Then,each sampling point is encoded by a multi-layer perceptron(MLP)to obtain the point feature.Furthermore,the global fea-ture and the local feature are concatenated with the point feature respectively and fed into the two de-coders respectively to obtain the magnitude and sign of the signed distance function(SDF)of the sam-pling point in the implicit field.Finally,the implicit surface of the object is obtained.The proposed method has performed 3D object reconstruction task on ShapeNet datasets with both qualitative and quantitative evaluations superior to state-of-the-art methods,especially for complex topological objects with holes and thin structures.
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