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A review of three dimensional reconstruction techniques

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Abstract

Three dimensional (3D) modeling is an important stereoscopic representation of an object for multiple viewpoints aggregation and geometrical information. A general 3D modeling pipeline consists of data acquisition, 3D reconstruction and surface reconstruction. The core computational process in 3D modeling is always associated with the 3D reconstruction, which can be categorized into three types, i.e. statistical models, discriminative learning models and generative learning models. Statistical models derive handcrafted feature descriptor from mathematical theory to extract the spatial and geometric features of 3D data. A corresponding matching is performed between multiple viewpoints 3D data to search for the maximum region likelihood across datasets and compute the best match of affine transformation. Discriminative learning models learn spatial coherent of 3D data through data-driven training that leads to the computation of affine transformation with data inferencing. Generative models, on the other hand, have the unsupervised capability of ingesting raw 3D data directly to learn latent representation of input 3D data and later generate ambient output sample from the latent representation. In this paper, a detailed comparison on the three types of 3D reconstruction techniques are reviewed in term of input data structure, correspondence accuracy, precision and recall using four benchmark datasets, i.e. ModelNet10/40, ICL-NUIM, and Semantic3D. The advantages and disadvantages of 3D reconstruction techniques are highlighted for implementation guideline and future improvement.

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Funding

This study is funded by Sarawak Multimedia Authority (SMA) with the project ID - SMA-1077. We would like to gratefully acknowledge the support of NVIDIA Corporation with the donation of the the Quadro P6000 GPU used for this research.

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Phang, J.T.S., Lim, K.H. & Chiong, R.C.W. A review of three dimensional reconstruction techniques. Multimed Tools Appl 80, 17879–17891 (2021). https://doi.org/10.1007/s11042-021-10605-9

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