Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks
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Summary
A framework which applies deep Convolutional Neural Networks on multiple 2D image views (or snapshots) of the point cloud using fully convolutional networks to label every 3D point is proposed.
- Type
- article
- Published
- 2017-01-01
- Cited by
- 330
- References
- 38
- Access
- Open access
- OpenAlex
- https://openalex.org/W2609946960
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:29771932
Keywords
Computer science, Point cloud, Segmentation, Cloud computing, Point (geometry)
References
- 3D URBAN GIS FROM LASER ALTIMETER AND 2D MAP DATA
- Recognising structure in laser scanning point clouds
- Spatially-sparse convolutional neural networks
- Multi-view Convolutional Neural Networks for 3D Shape Recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Fully convolutional networks for semantic segmentation
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- Efficient RANSAC for Point‐Cloud Shape Detection
- Fast plane detection in disparity maps
- Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation
- Unsupervised feature learning for 3D scene labeling
- Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
- Shape-based recognition of 3D point clouds in urban environments
- The ISPRS benchmark on urban object classification and 3D building reconstruction
- Piecewise‐Planar 3D Reconstruction with Edge and Corner Regularization
- On Fast Surface Reconstruction Methods for Large and Noisy Datasets
- AIRBORNE LIDAR FEATURE SELECTION FOR URBAN CLASSIFICATION USING RANDOM FORESTS
- Supervised Parametric Classification of Aerial LiDAR Data
- Are we ready for autonomous driving? The KITTI vision benchmark suite
- Fast geometric point labeling using conditional random fields
Cited by
- A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds
- SAR AND OBLIQUE AERIAL OPTICAL IMAGE FUSION FOR URBAN AREA IMAGESEGMENTATION
- A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds
- SEGCloud: Semantic Segmentation of 3D Point Clouds
- Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs
- Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds
- SnapNet-R: Consistent 3D Multi-view Semantic Labeling for Robotics
- SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks
- FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds
- Geospatial Computer Vision Based on Multi-Modal Data - How Valuable Is Shape Information for the Extraction of Semantic Information?
- An octree cells occupancy geometric dimensionality descriptor for massive on-server point cloud visualisation and classification
- FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
- Classification of Point Cloud Scenes with Multiscale Voxel Deep Network
- Tangent Convolutions for Dense Prediction in 3D
- Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping
- A SUPER VOXEL-BASED RIEMANNIAN GRAPH FOR MULTI SCALE SEGMENTATION OF LIDAR POINT CLOUDS
- Large-scale 3D Point Cloud Classification Based On Feature Description Matrix By CNN
- DEEP MULTI-TASK LEARNING FOR TREE GENERA CLASSIFICATION
- FOREST COVER CLASSIFICATION USING GEOSPATIAL MULTIMODAL DATA
- Large-scale Machine Learning for Point Cloud Processing
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