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• W4313131036 abstract "Down-sampling algorithms are adopted to simplify the point clouds and save the computation cost on subsequent tasks. Existing learning-based sampling methods often need to train a big sampling network to support sampling under different resolutions, which must generate sampled points with the costly maximum resolution even if only low-resolution points need to be sampled. In this work, we propose a novel resolution-free point clouds sampling network to directly sample the original point cloud to different resolutions, which is conducted by optimizing non-learning-based initial sampled points to better positions. Besides, we introduce data distillation to assist the training process by considering the differences between task network outputs from original point clouds and sampled points. Experiments on point cloud reconstruction and recognition tasks demonstrate that our method can achieve SOTA performances with lower time and memory cost than existing learning-based sampling strategies. Codes are available at https://github.com/Tianxinhuang/PCDNet ." @default.
• W4313131036 created "2023-01-06" @default.
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• W4313131036 date "2022-01-01" @default.
• W4313131036 modified "2023-09-26" @default.
• W4313131036 title "Resolution-Free Point Cloud Sampling Network with Data Distillation" @default.
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• W4313131036 doi "https://doi.org/10.1007/978-3-031-20086-1_4" @default.
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