In this article, we proposed a fast algorithm of symmetry detection 3D model. First, we used the PCA algorithm for initial symmetry detection. Then, using exhaustive search of symmetry planes passing through the center of gravity about the initial symmetry plane, we determined the optimal symmetry plane with the help of the modified Hausdorff metric. The accuracy and speed of the proposed symmetry detection algorithm on real and synthetic data are compared to the PCA algorithm.

Fast 3D object symmetry detection for point cloud

Abstract. In this paper we present our novel approach for acquiring and managing digital models of archaeological sites, and the visualization techniques used to showcase them. In particular, we will demonstrate two technologies: our robotic system for digitization of archaeological sites (DigiRo) result of over three years of efforts by a group of cultural heritage experts, computer scientists and roboticists, and our cloud-based archaeological information system (ARIS). Finally we describe the viewers we developed to inspect and navigate the 3D models: a viewer for the web (ROVINA Web Viewer) and an immersive viewer for Virtual Reality (ROVINA VR Viewer).

Robotic and Virtual Reality for cultural heritage digitization and fruition

Unmanned aerial vehicles and drones are flying objects carrying small payloads for various applications, including agriculture, photography for weddings & religious ceremonies, e-commerce deliveries, remote sensing, 3D mapping, surveillance and defense. These flying objects also called as drones are available in two variants; 1. Fixed wing propeller type, and 2. Multi-wings propeller type. Nowadays, UAVs and Drones are the primary threat to the national security on the mainland and the line of actual control. Generally, these threat-rising drones can be eradicated by anti-missile techniques, which are not economical however will destroy them. The objective of the work here is to design and develop a ballistic drone capturing system comprising two main steps; 1. classification of UAVs from the flying birds and 2. Capturing the UAVs without causing significant damage to the drone. Here, the methodology uses computer-generated infrared images of the drone in the pretrained network architecture, which acts as the Convolution Neural Network (CNN) for classifying drones and birds. The performance of AlexNet and ResNet-50 performs better and it shows 100 percent efficiency and small distribution with more stability. In the near future, the ballistic capturing system will be designed and developed as a product, which would support acquiring data about malicious drones.

Detection and Classification in Ballistic Drone Capturing Based on Transfer Learning Networks Using Infrared Images

Accurate measurement of symmetry plane is very important to the research field of industrial detection. A solution model of the symmetry plane of vehicle body is constructed by the improved expectation–maximization (IEM) method. The coarse-to-fine process and the $M$ -neighborhood method are developed to achieve the parallel computing process and enhance the detection efficiency, according to the vehicle point cloud. In order to verify the measurement accuracy, a 3-D point-cloud dataset is established with different vehicle bodies. As the vehicle bodies in the dataset derive from program models, the symmetry plane is known in the modeling process. An evaluation method with the projection angles for the vehicle-body symmetry plane is described for the wheel alignment measurement benchmark. The method is further compared with other methods, on the basis of the vehicle dataset and evaluation indicators. Moreover, the physical, absolute benchmark of the symmetry plane, which is generated from the high-precision rotation table of the verification instrument, conducts the experimental verification of the solution method of the symmetry plane.

Accurate Measurement and Evaluation Methodology of Vehicle-Body Symmetry Plane Verified by Absolute Benchmark

Accurate measurement of symmetry plane is very important to the research field of industrial detection. A solution model of the symmetry plane of vehicle body is constructed by the improved expectation maximization (IEM) method. The coarse-to-fine process and the M-neighbourhood method are developed to achieve the parallel computing process and enhance the detection efficiency, according to the vehicle point cloud. In order to verify the measurement accuracy, a 3D point-cloud dataset is established with different vehicle bodies. As the vehicle bodies in the dataset derive from program models, the symmetry plane is known in the modeling process. An evaluation method with the projection angles for the vehicle body symmetry plane is described for the wheel alignment measurement benchmark. The method is further compared with other methods, on the basis of the vehicle dataset and evaluation indicators. Moreover, the physical, absolute benchmark of the symmetry plane, which is generated from the high precision rotation-table of the verification instrument, conducts the experimental verification of the solution method of the symmetry plane. 

Cultural Heritage (CH) artifacts generally possess symmetry of reflection, rotation, translation and glide reflection in their shape. Similarity measures are used to determine complex 3D models where symmetry is considered to be one of the similarity signatures. This work presents the methodology of detecting symmetry in 3D objects based on the three techniques: (1) Eigenvalues and Eigenvectors, (2) local surface discontinuity, and (3) pixel orientation. In this work, these methods have been modified suitably for the detection of symmetry in CH artifacts. Among these methods, it is found that the first two methods yield better performance on the symmetry signature estimation of 98 percent for complex models and up to 100% for primitive models. The execution time of the proposed methods is compared with the state-of-the-art approaches available in the literature. Three levels of random 3D models available in the internet repository are analyzed for efficiency, performance and robustness. At each level, the accuracy of the Eigenvalue method and the local discontinuity method is found to be better than the pixel orientation method. The modified algorithms have been tested for better performance with F-score, robustness, and execution time with 3D benchmark dataset and cultural heritage dataset available in the literature. Future work shall be extended by applying the symmetry features as constraints for the effective search of CH artifacts in digital repositories.

Rajkumar Gothandaraman

Papers

Virtual models in 3D digital reconstruction: detection and analysis of symmetry