Semantic segmentation of 3D scenes is a fundamental problem in 3D computer vision. In this paper, we propose a deep neural network for 3D semantic segmentation of raw point clouds. A multi-scale feature learning block is first introduced to obtain informative contextual features in 3D point clouds. A global and local feature aggregation block is then extended to improve the feature learning ability of the network. Based on these strategies, a powerful architecture named 3DMAX-Net is finally provided for semantic segmentation in raw 3D point clouds. Experiments have been conducted on the Stanford large-scale 3D Indoor Spaces Dataset using only geometry information. Experimental results have clearly shown the superiority of the proposed network.

This paper presents a global localization method for mobile robots based on the geometric information of structured indoor environments. With a global/local point cloud and projection map, lines are extracted from the projection maps using Hough transform. According to the directions of the obtained lines, the orientations of projection maps and point clouds are normalized. Next, the template matching algorithm is applied to the normalized global and local projection maps. Once coarse localization is completed, final accurate localization is achieved using the Iterative Closest Points (ICP) algorithm. Experimental results on several point clouds show that the proposed method can achieve high localization accuracy in real-time. The proposed method can be used for other global localization applications in structured environments.

Fully automatic 3-D point cloud registration is a highly challenging task in light detection and ranging (LiDAR) remote sensing. The coherent point drift (CPD) algorithm provides an appropriate solution for point cloud registration because of its high accuracy. However, real application of the traditional CPD algorithm is limited due to its demanding computational complexity. In this letter, we present a novel accelerated CPD (ACPD) algorithm for fast, accurate, and automatic registration of 3-D point clouds. First, a global squared iterative expectation–maximization (gSQUAREM) technique is integrated to the ACPD algorithm. Then, the dual-tree improved fast Gauss transform method is used to further accelerate the Gaussian summation process during the correspondence probability matrix calculation. Experimental results on two real data sets show that the proposed algorithm can perform fast and accurate registration on LiDAR point clouds.

In this paper, classification via joint sparse representation of the monogenic signal is presented for target recognition in SAR imagery. First, the monogenic signal is performed to capture the characteristics of SAR image. Since it is infeasible to directly apply the raw component to classification due to the high data dimension and redundancy, three augmented feature vectors are defined via uniform downampling of the real part, the imagery part, and the instantaneous phase. The monogenic features are then fed into a recently developed framework, sparse representation-based classification (SRC). Rather than produce individual sparse pattern, this paper generates the similar sparsity pattern for three feature vectors by imposing a mixed norm on the representation matrix. Extensive experiments on MSTAR database demonstrate that the proposed method could significantly improve the recognition accuracy.

In this paper, the classification via nonnegative and local linear regression model is proposed for SAR image-based target recognition. Recently, a simple yet effective method, linear regression for pattern recognition has been presented. By assuming that images from a single-object class lie on a linear subspace, it represents the test image as a linear combination of class-specific galleries. The representation is obtained by solving a typical inverse problem with least-square strategy. Since the negative weights play a counteractive role in reconstruction, it may be unreasonable to generate the negative weights. In addition, those elements close to the test sample should contribute much more than the ones far from the test. Thus this paper limits the feasible set of the representation by nonnegative and locality constraint. The decision is ruled in favor of the class with the minimum reconstruction error. Extensive experiments on MSTAR database demonstrate that the proposed methods significantly improve the accuracy than the standard one.

Point-cloud registration plays a significant role in the area of mobile LiDAR data processing. This paper proposes an automatic markerless registration algorithm for lidar point-clouds. It first introduces a local feature for point-cloud representation. The feature is invariant to rotations and translations of a point-cloud. It then presents a point-cloud registration method using geometric consistency check and the Iterative Closest Points (ICP) algorithm. Comparative experiments were performed on a publicly available dataset. Experimental results show that our algorithm is very accurate and outperforms the spin image and SHOT based algorithms.

Most of methods for change detection of floods in SAR images are based on the difference image (DI), which is from comparing the flood image and the reference image straightforwardly. DI, however, is often distorted by the interferences of the complicated reference image. Meanwhile, the spatial contextual information of floods is difficult to be effectively used. In response to these problems, a novel change detection method by removing the interference of the reference image is proposed. Firstly, the difference image is segmented to derive the initial change mask, i.e. a part of flood areas. Secondly, the whole flood areas are gained by region growing directly in the flood image. Experimental results on the real ERS SAR dataset validate its effectiveness on both the quantitative and subjective aspects.

3D object recognition in cluttered scenes is a rapidly growing research area. Based on the used types of features, 3D object recognition methods can broadly be divided into two categories-global or local feature based methods. Intensive research has been done on local surface feature based methods as they are more robust to occlusion and clutter which are frequently present in a real-world scene. This paper presents a comprehensive survey of existing local surface feature based 3D object recognition methods. These methods generally comprise three phases: 3D keypoint detection, local surface feature description, and surface matching. This paper covers an extensive literature survey of each phase of the process. It also enlists a number of popular and contemporary databases together with their relevant attributes.

Range image registration is a fundamental research topic for 3D object modeling and recognition. In this paper, we propose an accurate and robust algorithm for pairwise and multi-view range image registration. We first extract a set of Rotational Projection Statistics (RoPS) features from a pair of range images, and perform feature matching between them. The two range images are then registered using a transformation estimation method and a variant of the Iterative Closest Point (ICP) algorithm. Based on the pairwise registration algorithm, we propose a shape growing based multi-view registration algorithm. The seed shape is initialized with a selected range image and then sequentially updated by performing pairwise registration between itself and the input range images. All input range images are iteratively registered during the shape growing process. Extensive experiments were conducted to test the performance of our algorithm. The proposed pairwise registration algorithm is accurate, and robust to small overlaps, noise and varying mesh resolutions. The proposed multi-view registration algorithm is also very accurate. Rigorous comparisons with the state-of-the-art show the superiority of our algorithm.

Recognizing 3D objects in the presence of clutter and occlusion is a challenging task. This paper presents a 3D free form object recognition system based on a novel local surface feature descriptor. For a randomly selected feature point, a local reference frame (LRF) is defined by calculating the eigenvectors of the covariance matrix of a local surface, and a feature descriptor called rotational projection statistics (RoPS) is constructed by calculating the statistics of the point distribution on 2D planes defined from the LRF. It finally proposes a 3D object recognition algorithm based on RoPS features. Candidate models and transformation hypotheses are generated by matching the scene features against the model features in the library, these hypotheses are then tested and verified by aligning the model to the scene. Comparative experiments were performed on two publicly available datasets and an overall recognition rate of 98.8% was achieved. Experimental results show that our method is robust to noise, mesh resolution variations and occlusion.