MVF-CNN: Fusion of Multilevel Features for Large-Scale Point Cloud Classification

Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point features, we propose a novel point set multi-level aggregation features extraction and fusion method based on multi-scale max pooling and latent Dirichlet allocation (LDA). To this end, in the hierarchical point set feature extraction, point sets of different levels and sizes are first adaptively generated through multi-level clustering. Then, more effective sparse representation is implemented by locality-constrained linear coding (LLC) based on single point features, which contributes to the extraction of discriminative individual point set features. Next, the local point set features are extracted by combining the max pooling method and the multi-scale pyramid structure constructed by the point’s coordinates within each point set. The global and the local features of the point sets are effectively expressed by the fusion of multi-scale max pooling features and global features constructed by the point set LLC-LDA model. The point clouds are classified by using the point set multi-level aggregation features. Our experiments on two scenes of airborne laser scanning (ALS) point clouds—a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud—demonstrate the effectiveness of the proposed point set multi-level aggregation features for point cloud classification, and the proposed method outperforms other related and compared algorithms.

“…mF 1 is computed by averaging all classes of the F 1 -scores [23]. More details of these metrics are presented in reference [34,35].…”

Section: Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification

Tong

Zhang

et al. 2019

“…In recent years, with the rapid advancement of computer vision and Light Detection and Ranging (lidar) technology, an increasing number of point clouds are acquired and widely used in various remote-sensing applications. In applications such as autonomous driving, understanding the outdoor scenes by semantic labeling of point clouds has become a hot topic [1][2][3][4]. Point cloud classification is to mark a specific semantic attribute label for each point in the point cloud [2], which is a key step in environmental perception and scene understanding.…”

Section: Introductionmentioning

confidence: 99%

“…For the past few years, the classification algorithms proposed in [3][4][5][6][7][8][9] have achieved good performances for classifying images and point clouds. For example, Zhang et al [10] proposed DKSVD (discriminative K-SVD [11]) algorithm, which introduces classification error to optimize feature extraction and classifier, simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Features Joint Learning with Label and Local Distribution Consistency for Point Cloud Classification

Tong

Chen

et al. 2020

Self Cite

In outdoor Light Detection and Ranging (lidar)point cloud classification, finding the discriminative features for point cloud perception and scene understanding represents one of the great challenges. The features derived from defect-laden (i.e., noise, outliers, occlusions and irregularities) and raw outdoor LiDAR scans usually contain redundant and irrelevant information which adversely affects the accuracy of point semantic labeling. Moreover, point cloud features of different views have a capability to express different attributes of the same point. The simplest way of concatenating these features of different views cannot guarantee the applicability and effectiveness of the fused features. To solve these problems and achieve outdoor point cloud classification with fewer training samples, we propose a novel multi-view features and classifiers’ joint learning framework. The proposed framework uses label consistency and local distribution consistency of multi-space constraints for multi-view point cloud features extraction and classification. In the framework, the manifold learning is used to carry out subspace joint learning of multi-view features by introducing three kinds of constraints, i.e., local distribution consistency of feature space and position space, label consistency among multi-view predicted labels and ground truth, and label consistency among multi-view predicted labels. The proposed model can be well trained by fewer training points, and an iterative algorithm is used to solve the joint optimization of multi-view feature projection matrices and linear classifiers. Subsequently, the multi-view features are fused and used for point cloud classification effectively. We evaluate the proposed method on five different point cloud scenes and experimental results demonstrate that the classification performance of the proposed method is at par or outperforms the compared algorithms.

“…The authors evaluated 7 types of domain definitions, 21 geometric feature definitions, 7 feature selection strategies, and 10 classifiers. Li et al [11] proposed a single point-based point cloud classification method based on multiscale voxels features fusion derived by the deep learning network. However, in single point-based methods, the representation of the geometric structure and contextual information in individual point clouds has not been fully used, resulting in lower accuracy of point labeling.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

Chen

et al. 2019

Self Cite

This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and higher-order conditional random fields (CRF) labeling. In the hierarchical clustering, the raw point clouds are over-segmented into a set of fine-grained clusters by integrating the point density clustering and the classic K-means clustering algorithm, followed by the proposed probability density clustering algorithm. Through this process, we not only obtain a more uniform size and more homogeneous clusters with semantic consistency, but the topological relationships of the cluster’s neighborhood are implicitly maintained by turning the problem of topology maintenance into a clustering problem based on the proposed probability density clustering algorithm. Subsequently, the fine-grained clusters and their topological context are fed into the CRF labeling step, from which the fine-grained cluster’s semantic labels are learned and determined by solving a multi-label energy minimization formulation, which simultaneously considers the unary, pairwise, and higher-order potentials. Our experiments of classifying urban and residential scenes demonstrate that the proposed approach reaches 88.5% and 86.1% of “m F 1 ” estimated by averaging all classes of the F 1 -scores. We prove that the proposed method outperforms five other state-of-the-art methods. In addition, we demonstrate the effectiveness of the proposed energy terms by using an “ablation study” strategy.