Deep Generative Modeling of LiDAR Data
暂无分享,去创建一个
Joelle Pineau | Herke van Hoof | Aaron C. Courville | Lucas Caccia | Joelle Pineau | H. V. Hoof | Lucas Caccia
[1] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[2] Lennart Svensson,et al. Fast LIDAR-based road detection using fully convolutional neural networks , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).
[3] Gabriel Kreiman,et al. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning , 2016, ICLR.
[4] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[5] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[6] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[7] Leonidas J. Guibas,et al. Representation Learning and Adversarial Generation of 3D Point Clouds , 2017, ArXiv.
[8] 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).
[9] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[10] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[11] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[12] Leonidas J. Guibas,et al. Learning Representations and Generative Models for 3D Point Clouds , 2017, ICML.
[13] Dong Tian,et al. FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[14] Adam Herout,et al. CNN for very fast ground segmentation in velodyne LiDAR data , 2017, 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).
[15] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[16] Ingmar Posner,et al. End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks , 2016, ArXiv.
[17] Ole Winther,et al. Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.
[18] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[19] Alexia Jolicoeur-Martineau,et al. The relativistic discriminator: a key element missing from standard GAN , 2018, ICLR.
[20] Wolfram Burgard,et al. Deep semantic classification for 3D LiDAR data , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[21] Heiko Hoffmann,et al. Sliced Wasserstein Distance for Learning Gaussian Mixture Models , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[22] Francesc Moreno-Noguer,et al. Deep Lidar CNN to Understand the Dynamics of Moving Vehicles , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[23] Forrest N. Iandola,et al. DSCnet: Replicating Lidar Point Clouds With Deep Sensor Cloning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[24] Yin Zhou,et al. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[25] Bo Li,et al. 3D fully convolutional network for vehicle detection in point cloud , 2016, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[26] Jason Yosinski,et al. An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution , 2018, NeurIPS.
[27] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[28] Léon Bottou,et al. Wasserstein GAN , 2017, ArXiv.
[29] Germán Ros,et al. CARLA: An Open Urban Driving Simulator , 2017, CoRL.
[30] Dushyant Rao,et al. Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[31] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[32] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[33] Carl Doersch,et al. Tutorial on Variational Autoencoders , 2016, ArXiv.
[34] Mathieu Aubry,et al. A Papier-Mache Approach to Learning 3D Surface Generation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[35] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[36] Han Zhang,et al. Self-Attention Generative Adversarial Networks , 2018, ICML.
[37] Hao Su,et al. A Point Set Generation Network for 3D Object Reconstruction from a Single Image , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[39] Francesc Moreno-Noguer,et al. Deconvolutional networks for point-cloud vehicle detection and tracking in driving scenarios , 2017, 2017 European Conference on Mobile Robots (ECMR).
[40] Tian Xia,et al. Vehicle Detection from 3D Lidar Using Fully Convolutional Network , 2016, Robotics: Science and Systems.