ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation

[1]  Vladlen Koltun,et al.  Playing for Data: Ground Truth from Computer Games , 2016, ECCV.

[2]  Kurt Keutzer,et al.  EmotionGAN: Unsupervised Domain Adaptation for Learning Discrete Probability Distributions of Image Emotions , 2018, ACM Multimedia.

[3]  Vittorio Murino,et al.  Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation , 2017, ICLR.

[4]  Rama Chellappa,et al.  Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.

[5]  Qingming Huang,et al.  Deep Unsupervised Convolutional Domain Adaptation , 2017, ACM Multimedia.

[6]  Jiwen Lu,et al.  DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[7]  Germán Ros,et al.  CARLA: An Open Urban Driving Simulator , 2017, CoRL.

[8]  Bichen Wu,et al.  SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation , 2020, ECCV.

[9]  Naveed Akhtar,et al.  Octree Guided CNN With Spherical Kernels for 3D Point Clouds , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[11]  Tomas Pfister,et al.  Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Chang Xu,et al.  Self-Supervised Representation Learning From Multi-Domain Data , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[13]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[14]  Shai Avidan,et al.  Learning to Sample , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Chao Chen,et al.  HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation , 2019, AAAI.

[16]  Wei Wu,et al.  PointCNN: Convolution On X-Transformed Points , 2018, NeurIPS.

[17]  Cyrill Stachniss,et al.  RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[18]  Geoffrey E. Hinton,et al.  Layer Normalization , 2016, ArXiv.

[19]  Kurt Keutzer,et al.  Multi-source Distilling Domain Adaptation , 2020, AAAI.

[20]  Kurt Keutzer,et al.  SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Alberto L. Sangiovanni-Vincentelli,et al.  A Review of Single-Source Deep Unsupervised Visual Domain Adaptation , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Kaiming He,et al.  Group Normalization , 2018, ECCV.

[23]  Cyrill Stachniss,et al.  SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[24]  Ulrich Neumann,et al.  Recurrent Slice Networks for 3D Segmentation of Point Clouds , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  James M. Rehg,et al.  Learning to Generate Synthetic Data via Compositing , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Bo Wang,et al.  Moment Matching for Multi-Source Domain Adaptation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[28]  Lei Wang,et al.  Appendix for : Graph Attention Convolution for Point Cloud Semantic Segmentation , 2019 .

[29]  Alberto L. Sangiovanni-Vincentelli,et al.  A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving , 2018, ICMR.

[30]  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.

[31]  Binh-Son Hua,et al.  ShellNet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[32]  Lennart Svensson,et al.  Fast LIDAR-based road detection using fully convolutional neural networks , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[33]  Kurt Keutzer,et al.  CycleEmotionGAN: Emotional Semantic Consistency Preserved CycleGAN for Adapting Image Emotions , 2019, AAAI.

[34]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[35]  Song Han,et al.  Point-Voxel CNN for Efficient 3D Deep Learning , 2019, NeurIPS.

[36]  Dariu M. Gavrila,et al.  Cross-Sensor Deep Domain Adaptation for LiDAR Detection and Segmentation , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[37]  Larry S. Davis,et al.  DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation , 2018, ECCV.

[38]  Kate Saenko,et al.  Return of Frustratingly Easy Domain Adaptation , 2015, AAAI.

[39]  Yi Li,et al.  Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[40]  C.-C. Jay Kuo,et al.  PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation , 2019, NeurIPS.

[41]  Leonidas J. Guibas,et al.  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.

[42]  Fabio Maria Carlucci,et al.  From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[43]  Kurt Keutzer,et al.  Multi-source Domain Adaptation for Semantic Segmentation , 2019, NeurIPS.

[44]  Michael I. Jordan,et al.  Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.

[45]  Martin Simonovsky,et al.  Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[46]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[47]  Srikanth Saripalli,et al.  LiDARNet: A Boundary-Aware Domain Adaptation Model for Lidar Point Cloud Semantic Segmentation , 2020, ArXiv.

[48]  Kurt Keutzer,et al.  Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources , 2021, WWW.

[50]  Taesung Park,et al.  CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.

[51]  Kurt Keutzer,et al.  LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[52]  Zhaoxiang Zhang,et al.  Scale-Aware Trident Networks for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[53]  Yue Wang,et al.  Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..

[54]  Xinming Huang,et al.  Learning to Segment 3D Point Clouds in 2D Image Space , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Ye Duan,et al.  PointGrid: A Deep Network for 3D Shape Understanding , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[56]  Carlos D. Castillo,et al.  Generate to Adapt: Aligning Domains Using Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[57]  Yifan Xu,et al.  SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters , 2018, ECCV.

[58]  Alexei A. Efros,et al.  Unsupervised Domain Adaptation through Self-Supervision , 2019, ArXiv.

[59]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[60]  José M. F. Moura,et al.  Adversarial Multiple Source Domain Adaptation , 2018, NeurIPS.

[61]  Victor S. Lempitsky,et al.  Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[62]  Jiaying Liu,et al.  Adaptive Batch Normalization for practical domain adaptation , 2018, Pattern Recognit..

[63]  Kate Saenko,et al.  Deep CORAL: Correlation Alignment for Deep Domain Adaptation , 2016, ECCV Workshops.

[64]  Namil Kim,et al.  Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[65]  Chi-Wing Fu,et al.  Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[66]  Tat-Seng Chua,et al.  Multi-source Domain Adaptation for Visual Sentiment Classification , 2020, AAAI.

[67]  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).

[68]  Philipp Krähenbühl,et al.  Free Supervision from Video Games , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[69]  Fabio Maria Carlucci,et al.  Domain Generalization by Solving Jigsaw Puzzles , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[70]  Julie Iskander,et al.  Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[71]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[72]  Lin Gao,et al.  VV-Net: Voxel VAE Net With Group Convolutions for Point Cloud Segmentation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[73]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[74]  Xiaogang Wang,et al.  Interpolated Convolutional Networks for 3D Point Cloud Understanding , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[75]  Gal Chechik,et al.  Self-Supervised Learning for Domain Adaptation on Point Clouds , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).

[76]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[77]  Jiaxin Li,et al.  SO-Net: Self-Organizing Network for Point Cloud Analysis , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[78]  Amin Zheng,et al.  RGCNN: Regularized Graph CNN for Point Cloud Segmentation , 2018, ACM Multimedia.