Line Based Multi-Range Asymmetric Conditional Random Field For Terrestrial Laser Scanning Data Classification

Terrestrial Laser Scanning (TLS) is a ground-based, active imaging method that rapidly acquires accurate, highly dense three-dimensional point cloud of object surfaces by laser range finding. For fully utilizing its benefits, developing a robust method to classify many objects of interests from huge amounts of laser point clouds is urgently required. However, classifying massive TLS data faces many challenges, such as complex urban scene, partial data acquisition from occlusion. To make an automatic, accurate and robust TLS data classification, we present a line-based multi-range asymmetric Conditional Random Field algorithm. The first contribution is to propose a line-base TLS data classification method. In this thesis, we are interested in seven classes: building, roof, pedestrian road (PR), tree, low man-made object (LMO), vehicle road (VR), and low vegetation (LV). The line-based classification is implemented in each scan profile, which follows the line profiling nature of laser scanning mechanism.Ten conventional local classifiers are tested, including popular generative and discriminative classifiers, and experimental results validate that the line-based method can achieve satisfying classification performance. However, local classifiers implement labeling task on individual line independently of its neighborhood, the inference of which often suffers from similar local appearance across different object classes. The second contribution is to propose a multi-range asymmetric Conditional Random Field (maCRF) model, which uses object context as post-classification to improve the performance of a local generative classifier. The maCRF incorporates appearance, local smoothness constraint, and global scene layout regularity together into a probabilistic graphical model. The local smoothness enforces that lines in a local area to have the same class label, while scene layout favours an asymmetric regularity of spatial arrangement between different object classes within long-range, which is considered both in vertical (above-bellow relation) and horizontal (front-behind) directions. The asymmetric regularity allows capturing directional spatial arrangement between pairwise objects (e.g. it allows ground is lower than building, not vice-versa). The third contribution is to extend the maCRF model by adding across scan profile context, which is called Across scan profile Multi-range Asymmetric Conditional Random Field (amaCRF) model. Due to the sweeping nature of laser scanning, the sequentially acquired TLS data has strong spatial dependency, and the across scan profile context can provide more contextual information. The final contribution is to propose a sequential classification strategy. Along the sweeping direction of laser scanning, amaCRF models were sequentially constructed. By dynamically updating posterior probability of common scan profiles, contextual information propagates through adjacent scan profiles

Contextual classification of point cloud data by exploiting individual 3d neigbourhoods

The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification

SEGCloud: Semantic Segmentation of 3D Point Clouds

3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets.Comment: Accepted as a spotlight at the International Conference of 3D Vision (3DV 2017

Multi-Scale Hierarchical Conditional Random Field for Railway Electrification Scene Classification Using Mobile Laser Scanning Data

With the recent rapid development of high-speed railway in many countries, precise inspection for railway electrification systems has become more significant to ensure safe railway operation. However, this time-consuming manual inspection is not satisfactory for the high-demanding inspection task, thus a safe, fast and automatic inspection method is required. With LiDAR (Light Detection and Ranging) data becoming more available, the accurate railway electrification scene understanding using LiDAR data becomes feasible towards automatic 3D precise inspection. This thesis presents a supervised learning method to classify railway electrification objects from Mobile Laser Scanning (MLS) data. First, a multi-range Conditional Random Field (CRF), which characterizes not only labeling homogeneity at a short range, but also the layout compatibility between different objects at a middle range in the probabilistic graphical model is implemented and tested. Then, this multi-range CRF model will be extended and improved into a hierarchical CRF model to consider multi-scale layout compatibility at full range. The proposed method is evaluated on a dataset collected in Korea with complex railway electrification systems environment. The experiment shows the effectiveness of proposed model

Multi-Modal Learning For Adaptive Scene Understanding

Modern robotics systems typically possess sensors of different modalities. Segmenting scenes observed by the robot into a discrete set of classes is a central requirement for autonomy. Equally, when a robot navigates through an unknown environment, it is often necessary to adjust the parameters of the scene segmentation model to maintain the same level of accuracy in changing situations. This thesis explores efficient means of adaptive semantic scene segmentation in an online setting with the use of multiple sensor modalities. First, we devise a novel conditional random field(CRF) inference method for scene segmentation that incorporates global constraints, enforcing particular sets of nodes to be assigned the same class label. To do this efficiently, the CRF is formulated as a relaxed quadratic program whose maximum a posteriori(MAP) solution is found using a gradient-based optimization approach. These global constraints are useful, since they can encode "a priori" information about the final labeling. This new formulation also reduces the dimensionality of the original image-labeling problem. The proposed model is employed in an urban street scene understanding task. Camera data is used for the CRF based semantic segmentation while global constraints are derived from 3D laser point clouds. Second, an approach to learn CRF parameters without the need for manually labeled training data is proposed. The model parameters are estimated by optimizing a novel loss function using self supervised reference labels, obtained based on the information from camera and laser with minimum amount of human supervision. Third, an approach that can conduct the parameter optimization while increasing the model robustness to non-stationary data distributions in the long trajectories is proposed. We adopted stochastic gradient descent to achieve this goal by using a learning rate that can appropriately grow or diminish to gain adaptability to changes in the data distribution

Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning and Higher Order MRF

【Abstract】Using mobile Light Detection and Ranging point clouds to accomplish road scene labeling tasks shows promise for a variety of applications. Most existing methods for semantic labeling of point clouds require a huge number of fully supervised point cloud scenes, where each point needs to be manually annotated with a specific category. Manually annotating each point in point cloud scenes is labor intensive and hinders practical usage of those methods. To alleviate such a huge burden of manual annotation, in this paper, we introduce an active learning method that avoids annotating the whole point cloud scenes by iteratively annotating a small portion of unlabeled supervoxels and creating a minimal manually annotated training set. In order to avoid the biased sampling existing in traditional active learning methods, a neighbor-consistency prior is exploited to select the potentially misclassified samples into the training set to improve the accuracy of the statistical model. Furthermore, lots of methods only consider short-range contextual information to conduct semantic labeling tasks, but ignore the long-range contexts among local variables. In this paper, we use a higher order Markov random field model to take into account more contexts for refining the labeling results, despite of lacking fully supervised scenes. Evaluations on three data sets show that our proposed framework achieves a high accuracy in labeling point clouds although only a small portion of labels is provided. Moreover, comparative experiments demonstrate that our proposed framework is superior to traditional sampling methods and exhibits comparable performance to those fully supervised models.10.13039/501100001809-National Natural Science Foundation of China; Collaborative Innovation Center of Haixi Government Affairs Big Data Sharin