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Point cloud segmentation is an important topic in 3D understanding that has traditionally has been tackled using either the CNN or Transformer.
A computer oriented geodetic data base; and a new technique in file sequencing
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Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, R. Howard, Wayne Hubbard, and Lawrence Jackel · 1989
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Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Pointpillars: Fast encoders for object detection from point clouds
Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom · 2019
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Kpconv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas · 2019
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nuscenes: A multimodal dataset for autonomous driving
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Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation
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Simplified state space layers for sequence modeling
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Octformer: Octree-based transformers for 3d point clouds
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Masked scene contrast: A scalable framework for unsupervised 3d representation learning
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Efficiently modeling long sequences with structured state spaces
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Stratified transformer for 3d point cloud segmentation
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