Proceedings of the conference on Visualization '01
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Robust Detection of Buildings in Digital Surface Models
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Automatic Generation of High-Quality Building Models from Lidar Data
IEEE Computer Graphics and Applications
ACM Transactions on Asian Language Information Processing (TALIP)
Scale Selection for Classification of Point-Sampled 3-D Surfaces
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Diagram Structure Recognition by Bayesian Conditional Random Fields
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Cluster Analysis and Priority Sorting in Huge Point Clouds for Building Reconstruction
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A volumetric fusion technique for surface reconstruction from silhouettes and range data
Computer Vision and Image Understanding
Conditional Random Field for 3D Point Clouds with Adaptive Data Reduction
CW '07 Proceedings of the 2007 International Conference on Cyberworlds
Image modeling using tree structured conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning and incorporating top-down cues in image segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Tree-structured conditional random fields for semantic annotation
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
On the reconstruction of height functions and terrain maps from dense range data
IEEE Transactions on Image Processing
Normal estimation for point clouds: a comparison study for a Voronoi based method
SPBG'05 Proceedings of the Second Eurographics / IEEE VGTC conference on Point-Based Graphics
Validating vision and robotic algorithms for dynamic real world environments
SIMPAR'10 Proceedings of the Second international conference on Simulation, modeling, and programming for autonomous robots
Conditional random fields for urban scene classification with full waveform LiDAR data
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
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In this paper, we propose a new method for 3D terrestrial laser range data classifications. This functions as the first step towards virtual city model reconstructions from range data and is particularly useful for scene understanding. Classification of the outdoor terrestrial range data into different data types (for example, building surface, vegetation and terrain) is challenging due to certain properties of the data: occlusions due to obstructions, density variation due to different distances of the scanned object from the laser scanner, multiple multi-structure objects and cluttered vegetation. Also, the range data acquired are massive in size and require a lot of computation and memory. Recognizing the redundancy of labeling every individual data, we propose over-segmenting the raw data into adaptive support regions: super-voxels. The super-voxels are computed using 3D scale theory and adapt to the above-mentioned range data properties. Colors and reflectance intensity acquired from the scanner system are combined with geometry features (saliency features and normals) that are extracted from the super-voxels, to form the feature descriptors for the supervised learning model. We proposed using the discriminative Conditional Random Fields for the classification problem and modified the model to incorporate multi-scales for super-voxel labeling. We validated our proposed strategy with synthetic data and real-world outdoor LIDAR (Light Detection and Ranging) data acquired from a Riegl LMS-Z420i terrestrial laser scanner. The results showed great improvement in the training and inference rate while maintaining comparable classification accuracy with previous approaches.