Automatic Alignment of 3D Multi-Sensor Point Clouds

Automatic 3D point cloud alignment is a major research topic in photogrammetry, computer vision and computer graphics. In this research, two keypoint feature matching approaches have been developed and proposed for the automatic alignment of 3D point clouds, which have been acquired from different sensor platforms and are in different 3D conformal coordinate systems. The first proposed approach is based on 3D keypoint feature matching. First, surface curvature information is utilized for scale-invariant 3D keypoint extraction. Adaptive non-maxima suppression (ANMS) is then applied to retain the most distinct and well-distributed set of keypoints. Afterwards, every keypoint is characterized by a scale, rotation and translation invariant 3D surface descriptor, called the radial geodesic distance-slope histogram. Similar keypoints descriptors on the source and target datasets are then matched using bipartite graph matching, followed by a modified-RANSAC for outlier removal. The second proposed method is based on 2D keypoint matching performed on height map images of the 3D point clouds. Height map images are generated by projecting the 3D point clouds onto a planimetric plane. Afterwards, a multi-scale wavelet 2D keypoint detector with ANMS is proposed to extract keypoints on the height maps. Then, a scale, rotation and translation-invariant 2D descriptor referred to as the Gabor, Log-Polar-Rapid Transform descriptor is computed for all keypoints. Finally, source and target height map keypoint correspondences are determined using a bi-directional nearest neighbour matching, together with the modified-RANSAC for outlier removal. Each method is assessed on multi-sensor, urban and non-urban 3D point cloud datasets. Results show that unlike the 3D-based method, the height map-based approach is able to align source and target datasets with differences in point density, point distribution and missing point data. Findings also show that the 3D-based method obtained lower transformation errors and a greater number of correspondences when the source and target have similar point characteristics. The 3D-based approach attained absolute mean alignment differences in the range of 0.23m to 2.81m, whereas the height map approach had a range from 0.17m to 1.21m. These differences meet the proximity requirements of the data characteristics and the further application of fine co-registration approaches

Semantic segmentation of outdoor scenes using LIDAR cloud point

In this paper we present a novel street scene semantic recognition framework, which takes advantage of 3D point clouds captured by a high definition LiDAR laser scanner. An important problem in object recognition is the need for sufficient labeled training data to learn robust classifiers. In this paper we show how to significantly re-duce the need for manually labeled training data by reduction of scene complexity using non-supervised ground and building segmentation. Our system first automatically seg-ments grounds point cloud, this is because the ground connects almost all other objects and we will use a connect component based algorithm to over segment the point clouds. Then, using binary range image processing building facades will be detected. Remained point cloud will grouped into voxels which are then transformed to super voxels. Local 3D features extracted from super voxels are classified by trained boosted decision trees and labeled with semantic classes e.g. tree, pedestrian, car. Given labeled 3D points cloud and 2D image with known viewing camera pose, the proposed association module aligned collections of 3D points to the groups of 2D image pixel to parsing 2D cubic images. One noticeable advantage of our method is the robustness to different lighting condition, shadows and city landscape. The proposed method is evaluated both quantitatively and qualitatively on a challenging fixed-position Terrestrial Laser Scanning (TLS) Velodyne data set and Mobile Laser Scanning (MLS), NAVTEQ True databases. Robust scene parsing results are reported

Discriminating deformable shape classes

We present and empirically test a novel approach for categorizing 3-D free form object shapes represented by range data. In contrast to traditional surface-signature based systems that use alignment to match specific objects, we adapted the newly introduced symbolic-signature representation to classify deformable shapes [10]. Our approach constructs an abstract description of shape classes using an ensemble of classifiers that learn object class parts and their corresponding geometrical relationships from a set of numeric and symbolic descriptors. We used our classification engine in a series of large scale discrimination experiments on two well-defined classes that share many common distinctive features. The experimental results suggest that our method outperforms traditional numeric signature-based methodologies. 1