Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis. Fei, B., Yang, W., Chen, W., Li, Z., Li, Y., Ma, T., Hu, X., & Ma, L. IEEE Transactions on Intelligent Transportation Systems, 2022.
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Paper doi abstract bibtex
Paper doi abstract bibtex Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field.
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abstract = {Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field.},
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author = {Fei, Ben and Yang, Weidong and Chen, Wen-Ming and Li, Zhijun and Li, Yikang and Ma, Tao and Hu, Xing and Ma, Lipeng},
doi = {10.1109/tits.2022.3195555},
journal = {IEEE Transactions on Intelligent Transportation Systems}
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