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Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception.
Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
Martin A. Fischler and Robert C. Bolles · 1981
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A density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu · 1996
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Slic superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk · 2012
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A. Efros · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Shuffle and learn: Unsupervised learning using temporal order verification
Ishan Misra, C. Lawrence Zitnick, and Martial Hebert · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2016
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The lovász-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks
Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Semantickitti: A dataset for semantic scene understanding of lidar sequences
Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Juergen Gall · 2019
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4d spatio-temporal convnets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
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Rangenet++: Fast and accurate lidar semantic segmentation
Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyrill Stachniss · 2019
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Self-supervised deep learning on point clouds by reconstructing space
Jonathan Sauder and Bjarne Sievers · 2019
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Training compute-optimal large language models
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 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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Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather
Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide · 2020
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds
Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Data-efficient image recognition with contrastive predictive coding
Olivier Henaff · 2020
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Randla-net: Efficient semantic segmentation of large-scale point clouds
Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham · 2020
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xmuda: Cross-modal unsupervised domain adaptation for 3d semantic segmentation
Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Emilie Wirbel, and Patrick Pérez · 2020
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Amvnet: Assertion-based multi-view fusion network for lidar semantic segmentation
Venice Erin Liong, Thi Ngoc Tho Nguyen, Sergi Widjaja, Dhananjai Sharma, and Zhuang Jie Chong · 2020
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Semanticposs: A point cloud dataset with large quantity of dynamic instances
Yancheng Pan, Biao Gao, Jilin Mei, Sibo Geng, Chengkun Li, and Huijing Zhao · 2020
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Self-supervised learning of point clouds via orientation estimation
Omid Poursaeed, Tianxing Jiang, Han Qiao, Nayun Xu, and Vladimir G. Kim · 2020
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Info3d: Representation learning on 3d objects using mutual information maximization and contrastive learning
Aditya Sanghi · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov · 2020
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Searching efficient 3d architectures with sparse point-voxel convolution
Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han · 2020
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Scan-based semantic segmentation of lidar point clouds: An experimental study
Larissa T Triess, David Peter, Christoph B Rist, and J Marius Zöllner · 2020
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Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Saining Xie, Jiatao Gu, Demi Guo, Charles R. Qi, Leonidas Guibas, and Or Litany · 2020
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Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation
Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka · 2020
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Polarnet: An improved grid representation for online lidar point clouds semantic segmentation
Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh · 2020
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Towards 3d lidar-based semantic scene understanding of 3d point cloud sequences: The semantickitti dataset
Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Jürgen Gall, and Cyrill Stachniss · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Polarstream: Streaming lidar object detection and segmentation with polar pillars
Qi Chen, Sourabh Vora, and Oscar Beijbom · 2021
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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
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Shape self-correction for unsupervised point cloud understanding
Ye Chen, Jinxian Liu, Bingbing Ni, Hang Wang, Jiancheng Yang, Ning Liu, Teng Li, and Qi Tian · 2021
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Af2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network
Ran Cheng, Ryan Razani, Ehsan Taghavi, Enxu Li, and Bingbing Liu · 2021
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Unsupervised semantic segmentation by contrasting object mask proposals
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool · 2021
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Are we hungry for 3d lidar data for semantic segmentation? a survey of datasets and methods
Biao Gao, Yancheng Pan, Chengkun Li, Sibo Geng, and Huijing Zhao · 2021
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Lidar-based panoptic segmentation via dynamic shifting network
Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, and Ziwei Liu · 2021
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Exploring data-efficient 3d scene understanding with contrastive scene contexts
Ji Hou, Benjamin Graham, Matthias Nießner, and Saining Xie · 2021
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Pri3d: Can 3d priors help 2d representation learning?
Ji Hou, Saining Xie, Benjamin Graham, Angela Dai, and Matthias Nießner · 2021
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Towards semantic segmentation of urban-scale 3d point clouds: A dataset, benchmarks and challenges
Weakly supervised 3d scene segmentation with region-level boundary awareness and instance discrimination
Kangcheng Liu, Yuzhi Zhao, Qiang Nie, Zhi Gao, and Ben M. Chen · 2022
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Less: Label-efficient semantic segmentation for lidar point clouds
Minghua Liu, Yin Zhou, Charles R. Qi, Boqing Gong, Hao Su, and Dragomir Anguelov · 2022
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Segcontrast: 3d point cloud feature representation learning through self-supervised segment discrimination
Lucas Nunes, Rodrigo Marcuzzi, Xieyuanli Chen, Jens Behley, and Cyrill Stachniss · 2022
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Gfnet: Geometric flow network for 3d point cloud semantic segmentation
Haibo Qiu, Baosheng Yu, and Dacheng Tao · 2022
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Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives
Giulia Rizzoli, Francesco Barbato, and Pietro Zanuttigh · 2022
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Qingyong Hu, Bo Yang, Sheikh Khalid, Wen Xiao, Niki Trigoni, and Andrew Markham · 2021
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Spatio-temporal self-supervised representation learning for 3d point clouds
Siyuan Huang, Yichen Xie, Song-Chun Zhu, and Yixin Zhu · 2021
Cited alongside, same era.
Efficient visual pretraining with contrastive detection
Olivier J. Hénaff, Skanda Koppula, Jean-Baptiste Alayrac, Aaron Van den Oord, Oriol Vinyals, and Joao Carreira · 2021
Cited alongside, same era.
Rellis-3d dataset: Data, benchmarks and analysis
Peng Jiang, Philip Osteen, Maggie Wigness, and Srikanth Saripallig · 2021
Cited alongside, same era.
Coconets: Continuous contrastive 3d scene representations
Shamit Lal, Mihir Prabhudesai, Ishita Mediratta, Adam W. Harley, and Katerina Fragkiadaki · 2021
Cited alongside, same era.
Learning from 2d: Contrastive pixel-to-point knowledge transfer for 3d pretraining
Yueh-Cheng Liu, Yu-Kai Huang, Hung-Yueh Chiang, Hung-Ting Su, Zhe-Yu Liu, Chin-Tang Chen, Ching-Yu Tseng, and Winston H. Hsu · 2021
Cited alongside, same era.
Generative zero-shot learning for semantic segmentation of 3d point clouds
Björn Michele, Alexandre Boulch, Gilles Puy, Maxime Bucher, and Renaud Marlet · 2021
Cited alongside, same era.
Gipso: Geometrically informed propagation for online adaptation in 3d lidar segmentation
Cristiano Saltori, Evgeny Krivosheev, Stéphane Lathuiliére, Nicu Sebe, Fabio Galasso, Giuseppe Fiameni, Elisa Ricci, and Fabio Poiesi · 2022
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Image-to-lidar self-supervised distillation for autonomous driving data
Corentin Sautier, Gilles Puy, Spyros Gidaris, Alexandre Boulch, Andrei Bursuc, and Renaud Marlet · 2022
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Weakly supervised segmentation on outdoor 4d point clouds with temporal matching and spatial graph propagation
Hanyu Shi, Jiacheng Wei, Ruibo Li, Fayao Liu, and Guosheng Lin · 2022
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Self-supervised learning with multi-view rendering for 3d point cloud analysis
Bach Tran, Binh-Son Hua, Anh Tuan Tran, and Minh Hoai · 2022
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Analyzing deep learning representations of point clouds for real-time in-vehicle lidar perception
Marc Uecker, Tobias Fleck, Marcel Pflugfelder, and J. Marius Zöllner · 2022
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Scribble-supervised lidar semantic segmentation
Ozan Unal, Dengxin Dai, and Luc Van Gool · 2022
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Transfer learning from synthetic to real lidar point cloud for semantic segmentation
Aoran Xiao, Jiaxing Huang, Dayan Guan, Fangneng Zhan, and Shijian Lu · 2022
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Simmim: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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Mingye Xu, Mutian Xu, Tong He, Wanli Ouyang, Yali Wang, Xiaoguang Han, and Yu Qiao · 2022
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Point cloud pre-training with natural 3d structures
Ryosuke Yamada, Hirokatsu Kataoka, Naoya Chiba, Yukiyasu Domae, and Tetsuya Ogata · 2022
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Implicit autoencoder for point cloud self-supervised representation learning
Siming Yan, Zhenpei Yang, Haoxiang Li, Li Guan, Hao Kang, Gang Hua, and Qixing Huang · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer · 2022
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Clip2scene: Towards label-efficient 3d scene understanding by clip
Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, and Wenping Wang · 2023
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Cross-modal learning for domain adaptation in 3d semantic segmentation
Maximilian Jaritz, Tuan-Hung Vu, Raoul De Charette, Émilie Wirbel, and Patrick Pérez · 2023
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Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
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Daps3d: Domain adaptive projective segmentation of 3d lidar point clouds
Alexey Klokov, Di Un Pak, Aleksandr Khorin, Dmitry Yudin, Leon Kochiev, Vladimir Luchinskiy, and Vitaly Bezuglyj · 2023
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Rethinking range view representation for lidar segmentation
Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, and Ziwei Liu · 2023
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Robo3d: Towards robust and reliable 3d perception against corruptions
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Conda: Unsupervised domain adaptation for lidar segmentation via regularized domain concatenation
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Lasermix for semi-supervised lidar semantic segmentation
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Semi-supervised lidar semantic segmentation with spatial consistency training
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Less is more: Reducing task and model complexity for 3d point cloud semantic segmentation
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Self-supervised image-to-point distillation via semantically tolerant contrastive loss
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Saluda: Surface-based automotive lidar unsupervised domain adaptation
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OpenAI · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023
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Using a waffle iron for automotive point cloud semantic segmentation
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Seggpt: Segmenting everything in context
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Unsupervised point cloud representation learning with deep neural networks: A survey
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3d semantic segmentation in the wild: Learning generalized models for adverse-condition point clouds
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A simple framework for open-vocabulary segmentation and detection
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Pids: Joint point interaction-dimension search for 3d point cloud
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Semanticflow: Semantic segmentation of sequential lidar point clouds from sparse frame annotations
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Generalized decoding for pixel, image, and language
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Segment everything everywhere all at once
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