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Transformers have demonstrated impressive results for 3D point cloud semantic segmentation.
nuScenes: A multimodal dataset for autonomous driving
Caesar, H.; Bankiti, V.; Lang, A. H.; Baldan, G.; and Beijbom, O. 2019 · 1903
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Generating long sequences with sparse transformers
Child, R.; Gray, S.; Radford, A.; and Sutskever, I. 2019 · 1904
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Neubegründung der mathematik. erste mitteilung
Hilbert, D.; and Hilbert, D. 1935 · 1935
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A computer oriented geodetic data base and a new technique in file sequencing
Morton, G. M. 1966 · 1966
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Nearest neighbor pattern classification
Cover, T.; and Hart, P. 1967 · 1967
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Sur une courbe, qui remplit toute une aire plane
Peano, G.; and Peano, G. 1990 · 1990
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Cylinder3d: An effective 3d framework for driving-scene lidar semantic segmentation
Zhou, H.; Zhu, X.; Song, X.; Ma, Y.; Wang, Z.; and Lin, D. 2020 · 2008
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
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Vision meets robotics: The kitti dataset
Geiger, A.; Lenz, P.; Stiller, C.; and Urtasun, R. 2013 · 2013
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Voxnet: A 3d convolutional neural network for real-time object recognition
Maturana, D.; and Scherer, S. 2015 · 2015
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3d shapenets: A deep representation for volumetric shapes
Wu, Z.; and Xiao, J. 2015 · 2015
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3d semantic parsing of large-scale indoor spaces
Armeni, I.; Sener, O.; Zamir, A. R.; Brilakis, I.; Fischer, M.; and Savarese, S. 2016 · 2016
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A scalable active framework for region annotation in 3d shape collections
Yi, L.; Kim, V. G.; Sheffer, A.; and Guibas, L. 2016 · 2016
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ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
Dai, A.; Chang, A. X.; Savva, M.; Halber, M.; Funkhouser, T.; and Nießner, M. 2017 · 2017
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Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
Berman, M.; Triki, A. R.; and Blaschko, M. B. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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3d semantic segmentation with submanifold sparse convolutional networks
Graham, B.; Engelcke, M.; and Van Der Maaten, L. 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A.; Salimans, T.; Sutskever, I.; et al. 2018 · 2018
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4d spatio-temporal convnets: Minkowski convolutional neural networks
Choy, C.; and Savarese, S. 2019 · 2019
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Kpconv: Flexible and deformable convolution for point clouds
Thomas, H.; Qi, C. R.; Goulette, F.; and Guibas, L. J. 2019 · 2019
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Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
Uy, M. A.; Pham, Q.-H.; Hua, B.-S.; Nguyen, D. T.; and Yeung, S.-K. 2019 · 2019
Cited alongside, same era.
Dynamic graph cnn for learning on point clouds
Wang, Y.; Sun, Y.; Liu, Z.; Sarma, S. E.; Bronstein, M. M.; and Solomon, J. M. 2019 · 2019
Cited alongside, same era.
Pointconv: Deep convolutional networks on 3d point clouds
Wu, W.; Qi, Z.; and Fuxin, L. 2019 · 2019
Cited alongside, same era.
Deep learning-based smart task assistance in wearable augmented reality
Park, K.-B.; Kim, M.; and Lee, J. Y. 2020 · 2020
Cited alongside, same era.
Conditional positional encodings for vision transformers
Chu, X.; Tian, Z.; Zhang, B.; Wang, X.; and Shen, C. 2021 · 2021
Cited alongside, same era.
Patchformer: An efficient point transformer with patch attention
Zhang, C.; Wan, H.; Shen, X.; and Wu, Z. 2022 · 2022
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Pointvector: a vector representation in point cloud analysis
Deng, X.; Zhang, W.; Ding, Q.; and Zhang, X. 2023 · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Gu, A.; and Dao, T. 2023 · 2023
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Spherical transformer for lidar-based 3d recognition
Lai, X.; Chen, Y.; Lu, F.; Liu, J.; and Jia, J. 2023 · 2023
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Meta architecture for point cloud analysis
Lin, H.; Zheng, X.; Li, L.; Chao, F.; Wang, S.; Wang, Y.; Tian, Y.; and Ji, R. 2023 · 2023
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Flatformer: Flattened window attention for efficient point cloud transformer
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Deng, J.; Shi, S.; Li, P.; Zhou, W.; Zhang, Y.; and Li, H. 2021 · 2021
Cited alongside, same era.
Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset
Ettinger, S.; Cheng, S.; Qi, C. R.; Zhou, Y.; et al. 2021 · 2021
Cited alongside, same era.
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
Fong, W. K.; Mohan, R.; Caesar, H.; and Beijbom, O. 2021 · 2021
Cited alongside, same era.
Combining recurrent, convolutional, and continuous-time models with linear state space layers
Gu, A.; Johnson, I.; Goel, K.; Rudra, A.; and Ré, C. 2021 · 2021
Cited alongside, same era.
Pct: Point cloud transformer
Guo, M.-H.; Cai, J.-X.; and Hu, S.-M. 2021 · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z.; Lin, Y.; Cao, Y.; Lin, S.; and Guo, B. 2021 · 2021
Cited alongside, same era.
Do vision transformers see like convolutional neural networks?
Raghu, M.; Unterthiner, T.; Kornblith, S.; Zhang, C.; and Dosovitskiy, A. 2021 · 2021
Cited alongside, same era.
Liu, Z.; Yang, X.; Tang, H.; Yang, S.; and Han, S. 2023 · 2023
Later among the works it cites.
Query refinement transformer for 3d instance segmentation
Lu, J.; Deng, J.; and Wang, C. 2023 · 2023
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Comparing the Locality Preservation of Z-order Curves and Hilbert Curves
Nordin, A.; and Telles, A. 2023 · 2023
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Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds
Seita, D.; Wang, Y.; Shetty, S. J.; Erickson, Z.; and Held, D. 2023 · 2023
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Llama: Open and efficient foundation language models
Touvron, H.; Lavril, T.; Izacard, G.; Azhar, F.; et al. 2023 · 2023
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Octformer: Octree-based transformers for 3d point clouds
Wang, P.-S. 2023 · 2023
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Swin3d: A pretrained transformer backbone for 3d indoor scene understanding
Yang, Y.-Q.; Guo, Y.-X.; Tong, X.; and Guo, B. 2023 · 2023
Later among the works it cites.
Graph Mamba: Towards Learning on Graphs with State Space Models
Behrouz, A.; and Hashemi, F. 2024 · 2024
Closest in time.
ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding
Duan, L.; Zhao, S.; Xue, N.; and Tao, D. 2024 · 2024
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MambaIR: A Simple Baseline for Image Restoration with State-Space Model
Guo, H.; Li, J.; Dai, T.; Ouyang, Z.; Ren, X.; and Xia, S.-T. 2024 · 2024
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PointMamba: A Simple State Space Model for Point Cloud Analysis
Liang, D.; Zhou, X.; and Bai, X. 2024 · 2024
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EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba
Pei, X.; Huang, T.; and Xu, C. 2024 · 2024
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Point Transformer V3: Simpler Faster Stronger
Wu, X.; Jiang, L.; Ouyang, W.; He, T.; and Zhao, H. 2024 · 2024
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Vivim: a Video Vision Mamba for Medical Video Object Segmentation
Yang, Y.; Xing, Z.; and Zhu, L. 2024 · 2024
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Point Cloud Mamba: Point Cloud Learning via State Space Model
Zhang, T.; Li, X.; Yuan, H.; Ji, S.; and Yan, S. 2024 · 2024
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Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Zhu, L.; Liao, B.; Zhang, Q.; Wang, X.; Liu, W.; and Wang, X. 2024 · 2024
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