Abstract:
Point cloud completion is able to estimate the complete point cloud starting from the missing point cloud, which obtains higher quality point cloud data for widely used i...Show MoreMetadata
Abstract:
Point cloud completion is able to estimate the complete point cloud starting from the missing point cloud, which obtains higher quality point cloud data for widely used in remote sensing 3-D modeling, medical imaging, robot vision, etc. The challenge of point clouds mainly lies in the disordered and unstructured nature, which makes point cloud completion difficult. Point cloud completion research can be broadly categorized into traditional approaches and deep learning-based methods. Recently, intensive research has primarily focused on deep learning-based methods, given robustness and efficiency in processing the substantial missing data encountered in complex real world scenes. In addition, deep learning-based methods have higher generalization performance. To stimulate future research, this survey presents a comprehensive review of existing traditional and deep learning-based 3-D point cloud completion methods. This review conducts extensive examinations of each stage of the process, providing a compilation of famous datasets, metrics, and their respective characteristics. In addition, the impacts of subsequent downstream application tasks with or without completion are discussed, followed by some potential future issues in point cloud completion.
Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 17)
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- IEEE Keywords
- Index Terms
- Point Cloud ,
- Point Cloud Completion ,
- Remote Sensing ,
- Learning-based Methods ,
- Deep Learning-based Methods ,
- Real Scenes ,
- Missing Points ,
- Convolutional Neural Network ,
- Local Features ,
- Global Features ,
- Generative Adversarial Networks ,
- Latent Space ,
- Light Detection And Ranging ,
- Local Details ,
- Geometric Information ,
- Self-supervised Learning ,
- Potential Space ,
- Simultaneous Localization And Mapping ,
- Complete Algorithm ,
- Dense Point Cloud ,
- Shape Completion ,
- Sparse Point Cloud ,
- Semantic Annotation ,
- Earth Mover’s Distance ,
- Input Point Cloud ,
- Point Cloud Generation ,
- Alignment-based Methods ,
- Semantic Segmentation ,
- Voxel-based Methods ,
- Sparse Point
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Point Cloud ,
- Point Cloud Completion ,
- Remote Sensing ,
- Learning-based Methods ,
- Deep Learning-based Methods ,
- Real Scenes ,
- Missing Points ,
- Convolutional Neural Network ,
- Local Features ,
- Global Features ,
- Generative Adversarial Networks ,
- Latent Space ,
- Light Detection And Ranging ,
- Local Details ,
- Geometric Information ,
- Self-supervised Learning ,
- Potential Space ,
- Simultaneous Localization And Mapping ,
- Complete Algorithm ,
- Dense Point Cloud ,
- Shape Completion ,
- Sparse Point Cloud ,
- Semantic Annotation ,
- Earth Mover’s Distance ,
- Input Point Cloud ,
- Point Cloud Generation ,
- Alignment-based Methods ,
- Semantic Segmentation ,
- Voxel-based Methods ,
- Sparse Point
- Author Keywords