Range-Image: Incorporating Sensor Topology for Li-DAR Point Cloud Processing. Photogrammetric Engineering & Remote Sensing, vol.84, p.3, 2018. ,
RIUNet: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud, vol.3, p.5, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02136459
Analysis of Efficient CNN Design Techniques for Semantic Segmentation, Conference on Computer Vision and Pattern Recognition, pp.663-672, 2018. ,
Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, issue.4, pp.834-848, 2018. ,
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, European Conference on Computer Vision, pp.801-808, 2018. ,
Fast Plane Extraction in Organized Point Clouds Using Agglomerative Hierarchical Clustering, International Conference on Robotics and Automation, vol.1, pp.6218-6225, 2014. ,
Are We Ready for Autonomous Driving? the KITTI Vision Benchmark Suite, Conference on Computer Vision and Pattern Recognition, vol.2, p.5, 2012. ,
Lidar-Based 3D Object Perception, Proceedings of international workshop on Cognition for Technical Systems, vol.1, pp.1-7, 2008. ,
Squeeze-And-Excitation Networks, Conference on Computer Vision and Pattern Recognition, pp.7132-7141, 2018. ,
, Squeezenet: AlexNetLevel Accuracy with 50x Fewer Parameters and ¡ 0.5 MB Model Size. In arXiv Preprint, 2016.
Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning, Conference on Computer Vision and Pattern Recognition, vol.2, p.4, 2019. ,
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs, Conference on Computer Vision and Pattern Recognition, pp.4558-4567, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01801186
3D Fully Convolutional Network for Vehicle Detection in Point Cloud, International Conference on Intelligent Robots and Systems, pp.1513-1518, 2017. ,
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation, Conference on Computer Vision and Pattern Recognition, pp.1925-1934, 2017. ,
, Focal loss for dense object detection, 2017.
Fully Convolutional Networks for Semantic Segmentation, Conference on Computer Vision and Pattern Recognition, pp.3431-3440, 2015. ,
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, Conference on Computer Vision and Pattern Recognition, pp.652-660, 2017. ,
U-Net: Convolutional Networks for Biomedical Image Segmentation, MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.234-241, 2005. ,
Pointseg: Real-Time Semantic Segmentation Based on 3D Li-DAR Point Cloud, arXiv Preprint, 2005. ,
Squeezeseg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D Li-DAR Point Cloud, International Conference on Robotics and Automation, p.5, 2004. ,
SqueezesegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud, vol.5, p.6, 2004. ,
ICNet for Real-Time Semantic Segmentation on HighResolution Images, European Conference on Computer Vision, pp.405-420, 2018. ,
VoxelNet: End-To-End Learning for Point Cloud Based 3D Object Detection, Conference on Computer Vision and Pattern Recognition, pp.4490-4499, 2018. ,