Wang et al., 2020 - Google Patents
Semantic-based building extraction from LiDAR point clouds using contexts and optimization in complex environmentWang et al., 2020
View HTML- Document ID
- 4660585435851539574
- Author
- Wang Y
- Jiang T
- Yu M
- Tao S
- Sun J
- Liu S
- Publication year
- Publication venue
- Sensors
External Links
Snippet
The extraction of buildings has been an essential part of the field of LiDAR point clouds processing in recent years. However, it is still challenging to extract buildings from huge amount of point clouds due to the complicated and incomplete structures, occlusions and …
- 238000000605 extraction 0 title abstract description 63
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
- G06F17/30595—Relational databases
- G06F17/30598—Clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/20—Image acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wang et al. | An improved DBSCAN method for LiDAR data segmentation with automatic Eps estimation | |
Aijazi et al. | Segmentation based classification of 3D urban point clouds: A super-voxel based approach with evaluation | |
Yang et al. | Segmentation and multi-scale convolutional neural network-based classification of airborne laser scanner data | |
Wang et al. | Semantic-based building extraction from LiDAR point clouds using contexts and optimization in complex environment | |
Jung et al. | Implicit regularization for reconstructing 3D building rooftop models using airborne LiDAR data | |
Li et al. | Roof plane segmentation from airborne LiDAR data using hierarchical clustering and boundary relabeling | |
Wang et al. | Multi-view fusion-based 3D object detection for robot indoor scene perception | |
Wang et al. | LiDAR filtering in 3D object detection based on improved RANSAC | |
Shin et al. | Semantic segmentation and building extraction from airborne LiDAR data with multiple return using PointNet++ | |
Wang et al. | Hierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds | |
Cao et al. | FEC: Fast Euclidean clustering for point cloud segmentation | |
Liao et al. | A supervoxel-based random forest method for robust and effective airborne LiDAR point cloud classification | |
Cai et al. | A building detection method based on semi-suppressed fuzzy C-means and restricted region growing using airborne LiDAR | |
Zhou et al. | A heuristic method for power pylon reconstruction from airborne LiDAR data | |
Su et al. | Building plane segmentation based on point clouds | |
Li et al. | A single point-based multilevel features fusion and pyramid neighborhood optimization method for ALS point cloud classification | |
Li et al. | Pole-like street furniture segmentation and classification in mobile LiDAR data by integrating multiple shape-descriptor constraints | |
Hui et al. | Building extraction from airborne lidar data based on multi-constraints graph segmentation | |
Xu et al. | Geometrical segmentation of multi-shape point clouds based on adaptive shape prediction and hybrid voting RANSAC | |
Xie et al. | Leaf-counting in monocot plants using deep regression models | |
Dai et al. | Soft segmentation of terrestrial laser scanning point cloud of forests | |
Ning et al. | Trunk-constrained and tree structure analysis method for individual tree extraction from scanned outdoor scenes | |
Feng et al. | Automating parameter learning for classifying terrestrial LiDAR point cloud using 2D land cover maps | |
Lei et al. | ALS point cloud classification by integrating an improved fully convolutional network into transfer learning with multi-scale and multi-view deep features | |
Dai et al. | Soft segmentation and reconstruction of tree crown from laser scanning data |