Pole-like objects provide important street infra- structure for road inventory and road mapping. In this article, we proposed a novel pole-like object extraction algorithm based on plane filtering from mobile Light Detection and Ranging (LiDAR) data. The proposed approach is composed of two parts. In the first part, a novel octree-based split scheme was proposed to fit initial planes from off-ground points. The results of the plane fitting contribute to the extraction of pole-like objects. In the second part, we proposed a novel method of pole-like object extraction by plane filtering based on local geometric feature restriction and isolation detection. The proposed approach is a new solution for detecting pole-like objects from mobile LiDAR data. The innovation in this article is that we assumed that each of the pole-like objects can be represented by a plane. Thus, the essence of extracting pole-like objects will be converted to plane selecting problem. The proposed method has been tested on three data sets captured from different scenes. The average completeness, correctness, and quality of our approach can reach up to 87.66%, 88.81%, and 79.03%, which is superior to state-of-the-art approaches. The experimental results indicate that our approach can extract pole-like objects robustly and efficiently.

The automatic classification of 3-D point clouds is publicly known as a challenging task in a complex road environment. Specifically, each point is automatically classified into a unique category label, and then, the labels are used as clues for semantic analysis and scene recognition. Instead of heuristically extracting handcrafted features in traditional methods to classify all points, we put forward an end-to-end octree-based fully convolutional network (FCN) to classify 3-D point clouds in an urban road environment. There are four contributions in this paper. The first is that the integration and comprehensive uses of OctNet and FCN greatly decrease the computing time and memory demands compared with a dense 3-D convolutional neural network (CNN). The second is that the octree-based network is strengthened by means of modifying the cross-entropy loss function to solve the problems of an unbalanced category distribution. The third is that an Inception-ResNet block is united with our network, which enables our 3-D CNN to effectively learn how to classify scenes containing objects at multiple scales and improve classification accuracy. The last is that an open source data set (HuangshiRoad data set) with ten different classes is introduced for 3-D point cloud classification. Three representative data sets [Semantic3D, WHU_MLS (blocks I and II), and HuangshiRoad] with different covered areas and numbers of points and classes are selected to evaluate our proposed method. The experimental results show that the overall classification accuracy is appreciable, with 89.4% for Semantic3D, 82.9% for WHU_MLS block I, 91.4% for WHU_MLS block II, and 94% for HuangshiRoad. Our deep learning approach can efficiently classify 3-D dense point clouds in an urban road environment measured by a mobile laser scanning (MLS) system or static LiDAR.

Several approaches to image stitching use different constraints to estimate the motion model between image pairs. These constraints can be roughly divided into two categories: geometric constraints and photometric constraints. In this paper, geometric and photometric constraints are combined to improve the alignment quality, which is based on the observation that these two kinds of constraints are complementary. On the one hand, geometric constraints (e.g., point and line correspondences) are usually spatially biased and are insufficient in some extreme scenes, while photometric constraints are always evenly and densely distributed. On the other hand, photometric constraints are sensitive to displacements and are not suitable for images with large parallaxes, while geometric constraints are usually imposed by feature matching and are more robust to handle parallaxes. The proposed method therefore combines them together in an efficient mesh-based image warping framework. It achieves better alignment quality than methods only with geometric constraints, and can handle larger parallax than photometric-constraint-based method. Experimental results on various images illustrate that the proposed method outperforms representative state-of-the-art image stitching methods reported in the literature.

In this paper, we present a method that stitches multiple videos captured with a fixed camera rig. We propose an Extended-MeshFlow motion model for video stitching. Firstly, uniform features are detected and matched at the overlapping region, from which the Exended-MeshFlow model is estimated. The model then warps the adjacent views to the common central view to eliminate the spatial misalignment. The motions located on the feature positions are interpolated to the mesh vertexes by Multilevel B-Spline Approximation(MBA). Collecting the motions on the vertexes form the vertex profiles, which are smoothed for temporal consistency. During the smoothing, only previous frames are required, thus the proposed method can stitch videos in an online mode. Experimental results on various of videos demonstrate that the proposed method can produce comparable stitching results in aspects of spatial alignment and temporal coherence.