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Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition
[article]
2021
arXiv
pre-print
Point cloud based retrieval for place recognition is still a challenging problem due to drastic appearance and illumination changes of scenes in changing environments. Existing deep learning based global descriptors for the retrieval task usually consume a large amount of computation resources (e.g., memory), which may not be suitable for the cases of limited hardware resources. In this paper, we develop an efficient point cloud learning network (EPC-Net) to form a global descriptor for visual
arXiv:2101.02374v1
fatcat:zm2mkiyp4vgojbt32ebpu5e3ji