SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
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[1] Hongsheng Li,et al. ST3D++: Denoised Self-Training for Unsupervised Domain Adaptation on 3D Object Detection , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Raoul de Charette,et al. Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3] G. Puy,et al. ALSO: Automotive Lidar Self-Supervision by Occupancy Estimation , 2022, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Yuexin Ma,et al. CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection , 2022, AAAI.
[5] Fabio Galasso,et al. CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation , 2022, ECCV.
[6] Mohan S. Kankanhalli,et al. Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation with Reliable Voted Pseudo Labels , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Haotian Tang,et al. TorchSparse: Efficient Point Cloud Inference Engine , 2022, MLSys.
[8] Jiwen Lu,et al. LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection , 2022, ECCV.
[9] Renaud Marlet,et al. Deep Surface Reconstruction from Point Clouds with Visibility Information , 2022, 2022 26th International Conference on Pattern Recognition (ICPR).
[10] Renaud Marlet,et al. POCO: Point Convolution for Surface Reconstruction , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] He Wang,et al. Domain Adaptation on Point Clouds via Geometry-Aware Implicits , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] H. Bischof,et al. The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[13] J. S. Berrio,et al. See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation , 2021, IEEE Robotics and Automation Letters.
[14] Rohit Mohan,et al. Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking , 2021, IEEE Robotics and Automation Letters.
[15] Eduardo R. Corral-Soto,et al. Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters , 2021, 2022 International Conference on Robotics and Automation (ICRA).
[16] Shijian Lu,et al. Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation , 2021, AAAI.
[17] Kilian Q. Weinberger,et al. Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection in Self-Driving Cars , 2021, 2022 International Conference on Robotics and Automation (ICRA).
[18] Dariu M. Gavrila,et al. Semantic Scene Completion Using Local Deep Implicit Functions on LiDAR Data , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Kevin Musgrave,et al. Benchmarking Validation Methods for Unsupervised Domain Adaptation , 2022, ArXiv.
[20] Y. Qiao,et al. ADAS: A Simple Active-and-Adaptive Baseline for Cross-Domain 3D Semantic Segmentation , 2022, ArXiv.
[21] Zhangyang Wang,et al. Point Cloud Domain Adaptation via Masked Local 3D Structure Prediction , 2022, ECCV.
[22] Venice Erin Liong,et al. ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation , 2021, 2023 IEEE International Conference on Robotics and Automation (ICRA).
[23] Geoffrey A. Hollinger,et al. Adversarial Training on Point Clouds for Sim-to-Real 3D Object Detection , 2021, IEEE Robotics and Automation Letters.
[24] Kui Jia,et al. Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[25] Charles R. Qi,et al. SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[26] Yulan Guo,et al. Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[27] Shijian Lu,et al. Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[28] Christoph B. Rist,et al. A Survey on Deep Domain Adaptation for LiDAR Perception , 2021, 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops).
[29] Dong Xu,et al. SRDAN: Scale-aware and Range-aware Domain Adaptation Network for Cross-dataset 3D Object Detection , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] José Marcato Junior,et al. Adversarial unsupervised domain adaptation for 3D semantic segmentation with multi-modal learning , 2021 .
[31] Srikanth Saripalli,et al. LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[32] Carlos Guindel,et al. Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View , 2021, 2021 IEEE International Intelligent Transportation Systems Conference (ITSC).
[33] Xiaoyuan Luo,et al. A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds , 2021, ArXiv.
[34] Xiaojuan Qi,et al. ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Nicolas Courty,et al. Unbalanced minibatch Optimal Transport; applications to Domain Adaptation , 2021, ICML.
[36] Ana Cristina Murillo,et al. Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions , 2020, ICINCO.
[37] Trevor Darrell,et al. ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation , 2020, AAAI.
[38] Thomas Funkhouser,et al. Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Gal Chechik,et al. Self-Supervised Learning for Domain Adaptation on Point Clouds , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).
[40] Trevor Darrell,et al. Tent: Fully Test-Time Adaptation by Entropy Minimization , 2021, ICLR.
[41] C. Stachniss,et al. Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[42] Dariu M. Gavrila,et al. SCSSnet: Learning Spatially-Conditioned Scene Segmentation on LiDAR Point Clouds , 2020, 2020 IEEE Intelligent Vehicles Symposium (IV).
[43] Cristiano Saltori,et al. SF-UDA 3D : Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection. , 2020 .
[44] Song Han,et al. Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution , 2020, ECCV.
[45] Matthias Bethge,et al. Improving robustness against common corruptions by covariate shift adaptation , 2020, NeurIPS.
[46] D. Sculley,et al. Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift , 2020, ArXiv.
[47] Yan Wang,et al. Train in Germany, Test in the USA: Making 3D Object Detectors Generalize , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Marc Pollefeys,et al. Convolutional Occupancy Networks , 2020, ECCV.
[49] Raoul de Charette,et al. xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Diane J. Cook,et al. A Survey of Unsupervised Deep Domain Adaptation , 2018, ACM Trans. Intell. Syst. Technol..
[51] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[52] C.-C. Jay Kuo,et al. PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation , 2019, NeurIPS.
[53] Dariu M. Gavrila,et al. Cross-Sensor Deep Domain Adaptation for LiDAR Detection and Segmentation , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).
[54] Silvio Savarese,et al. 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[55] Cyrill Stachniss,et al. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[56] Richard A. Newcombe,et al. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Sebastian Nowozin,et al. Occupancy Networks: Learning 3D Reconstruction in Function Space , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Hao Zhang,et al. Learning Implicit Fields for Generative Shape Modeling , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Patrick Pérez,et al. ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[60] Kurt Keutzer,et al. SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[61] Frank Hutter,et al. Decoupled Weight Decay Regularization , 2017, ICLR.
[62] Hongen Liao,et al. Efficient Semantic Scene Completion Network with Spatial Group Convolution , 2018, ECCV.
[63] Yang Zou,et al. Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training , 2018, ArXiv.
[64] Jiaying Liu,et al. Adaptive Batch Normalization for practical domain adaptation , 2018, Pattern Recognit..
[65] Nicolas Courty,et al. DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation , 2018, ECCV.
[66] Chuang Gan,et al. Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency , 2017, ECCV.
[67] Jung-Woo Ha,et al. StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[68] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.
[69] Michael I. Jordan,et al. Conditional Adversarial Domain Adaptation , 2017, NeurIPS.
[70] Luc Van Gool,et al. Deep Domain Adaptation by Geodesic Distance Minimization , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).
[71] Tatsuya Harada,et al. Asymmetric Tri-training for Unsupervised Domain Adaptation , 2017, ICML.
[72] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[73] Trevor Darrell,et al. Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[74] Michael I. Jordan,et al. Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.
[75] Kate Saenko,et al. Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.
[76] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[77] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[78] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[79] Michael I. Jordan,et al. Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.
[80] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[81] Andreas Geiger,et al. Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[82] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[83] Daniel Marcu,et al. Domain Adaptation for Statistical Classifiers , 2006, J. Artif. Intell. Res..