Abstract
Image-based 3D reconstruction is a fundamental task in computer vision and computer graphics. Although many existing approaches have obtained excellent results, they all dependent on multi-view images, then these methods are very complex. To reduce the complexity of the existing approaches, in this paper we fist propose a novel 3D reconstruction method based on single RGB image and the corresponding depth image, and design a unified framework called Single3D. Second, we trained a neural network model to reduce noises for improving the quality of the input RGB image, then leading a desirable RGB image. Third, we combine the deep guided filter and fast guided filter to fill holes on the depth image for improving the quality of 3D model. Fourth, we recover high-quality 3D model from the RGB-D image based on the proposed Single3D framework. Finally, the comprehensive experiments were conducted on the most challenging benchmarking dataset with variant light, occlusion, and clustering. Experimental results show that our method has desirable accuracy and efficiency.
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