LU-Net: A Simple Approach to 3D LiDAR Point Cloud Semantic Segmentation
暂无分享,去创建一个
[1] Aurélie Bugeau,et al. RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud , 2019, ArXiv.
[2] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[3] Jean-François Aujol,et al. Range-Image: Incorporating Sensor Topology for Lidar Point Cloud Processing , 2018, Photogrammetric Engineering & Remote Sensing.
[4] 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).
[5] 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).
[6] Vineet R. Kamat,et al. Fast plane extraction in organized point clouds using agglomerative hierarchical clustering , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[7] 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).
[8] 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).
[9] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[10] Yuan Wang,et al. PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud , 2018, ArXiv.
[11] Michael Himmelsbach,et al. LIDAR-based 3D Object Perception , 2008 .
[12] Mohamed Boussaha,et al. Point Cloud Oversegmentation With Graph-Structured Deep Metric Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Michael Himmelsbach,et al. LIDAR-based 3 D Object Perception , 2008 .
[14] Martin Simonovsky,et al. Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[15] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] 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.
[17] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[18] 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.
[19] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[20] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] Ian D. Reid,et al. RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Senthil Yogamani,et al. Analysis of Efficient CNN Design Techniques for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).