Histogram of distances for local surface description

3D object recognition is proven superior compared to its 2D counterpart with numerous implementations, making it a current research topic. Local based proposals specifically, although being quite accurate, they limit their performance on the stability of their local reference frame or axis (LRF/A) on which the descriptors are defined. Additionally, extra processing time is demanded to estimate the LRF for each local patch. We propose a 3D descriptor which overrides the necessity of a LRF/A reducing dramatically processing time needed. In addition robustness to high levels of noise and non-uniform subsampling is achieved. Our approach, namely Histogram of Distances is based on multiple L2-norm metrics of local patches providing a simple and fast to compute descriptor suitable for time-critical applications. Evaluation on both high and low quality popular point clouds showed its promising performance

A Novel Point Cloud Compression Algorithm for Vehicle Recognition Using Boundary Extraction

Recently, research on the hardware system for generating point cloud data through 3D LiDAR scanning has improved, which has important applications in autonomous driving and 3D reconstruction. However, point cloud data may contain defects such as duplicate points, redundant points, and an unordered mass of points, which put higher demands on the performance of hardware systems for processing data. Simplifying and compressing point cloud data can improve recognition speed in subsequent processes. This paper studies a novel algorithm for identifying vehicles in the environment using 3D LiDAR to obtain point cloud data. The point cloud compression method based on the nearest neighbor point and boundary extraction from octree voxels center points is applied to the point cloud data, followed by the vehicle point cloud identification algorithm based on image mapping for vehicle recognition. The proposed algorithm is tested using the KITTI dataset, and the results show improved accuracy compared to other methods

3D Automatic Target Recognition for Future LIDAR Missiles

We present a real-time three-dimensional automatic target recognition approach appropriate for future light detection and ranging-based missiles. Our technique extends the speeded-up robust features method into the third dimension by solving multiple two-dimensional problems and performs template matching based on the extreme case of a single pose per target. Evaluation on military targets shows higher recognition rates under various transformations and perturbations at lower processing time compared to state-of-the-art approaches

Rotational Projection Statistics for 3D Local Surface Description and Object Recognition

Recognizing 3D objects in the presence of noise, varying mesh resolution, occlusion and clutter is a very challenging task. This paper presents a novel method named Rotational Projection Statistics (RoPS). It has three major modules: Local Reference Frame (LRF) definition, RoPS feature description and 3D object recognition. We propose a novel technique to define the LRF by calculating the scatter matrix of all points lying on the local surface. RoPS feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2D planes and calculating a set of statistics (including low-order central moments and entropy) of the distribution of these projected points. Using the proposed LRF and RoPS descriptor, we present a hierarchical 3D object recognition algorithm. The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets. Our proposed techniques exhibited superior performance compared to existing techniques. We also showed that our method is robust with respect to noise and varying mesh resolution. Our RoPS based algorithm achieved recognition rates of 100%, 98.9%, 95.4% and 96.0% respectively when tested on the Bologna, UWA, Queen's and Ca' Foscari Venezia Datasets.Comment: The final publication is available at link.springer.com International Journal of Computer Vision 201

3D free form object recognition using rotational projection statistics

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

Construction Scene Point Cloud Acquisition, Object Finding and Clutter Removal in Real Time

Within industrial construction, piping can constitute up to 50% of the cost of a typical project. It has been shown that across the activities involved in pipe fabrication, pipe fitting has the highest impact on the critical path. The pipe fitter is responsible for interpreting the isometric drawing and then performing the tack welds on piping components so that the assembly complies with the design. Three main problems in doing this task are identified as: (1) reading and interpreting the isometric drawing is challenging and error prone for spatially complicated assemblies, (2) in assemblies with tight allowable tolerance, a number of iterations will take place to fit the pipes with compliance to the design. These iterations (rework) will remain unrecorded in the production process, and (3) no continuous measurement tool exists to let the fitter check his/her work in progress against the design information and acceptance specifications. Addressing these problems could substantially improve pipe fitters’ productivity. The objective of this research is to develop a software package integrating a threefold solution to simplify complex tasks involved in pipe fabrication: (1) making design information easier to understand, with the use of a tablet, 3D imaging device and an application software, (2) providing visual feedback on the correctness of fabrication between the design intent and the as-built state, and (3) providing frequent feedback on fabrication using a step-by-step assembly and control framework. The step-by-step framework will reduce the number of required iterations for the pipe fitter. A number of challenges were encountered in order to provide a framework to make real time, visual and frequent feedback. For frequent and visual feedback, a real time 3D data acquisition tool with an acceptable level of accuracy should be adopted. This is due to the speed of fabrication in an industrial facility. The second challenge is to find the object of interest in real time, once a point cloud is acquired, and finally, once the object is found, to optimally remove points that are considered as clutter to improve the visual feedback for the pipe fitters. To address the requirement for a reliable and real time acquisition tool, Chapter 3 explores the capabilities and limitations of low cost range cameras. A commercially available 3D imaging tool was utilized to measure its performance for real time point cloud acquisition. The device was used to inspect two pipe spools altered in size. The acquired point clouds were super-imposed on the BIM (Building Information Model) model of the pipe spools to measure the accuracy of the device. Chapter 4 adapts and examines a real time and automatic object finding algorithm to measure its performance with respect to construction challenges. Then, a K-Nearest Neighbor (KNN) algorithm was employed to classify points as being clutter or corresponding to the object of interest. Chapter 5 investigates the effect of the threshold value “K” in the K-Nearest Neighbor algorithm and optimizing its value for an improved visual feedback. As a result of the work described in this thesis, along with the work of two other master students and a co-op student, a software package was designed and developed. The software package takes advantage of the investigated real time point cloud acquisition device. While the object finding algorithm proved to be effective, a 3-point matching algorithm was used, as it was more intuitive for the users and took less time. The KNN algorithm was utilized to remove clutter points to provide more accurate visual feedback more accurate to the workers