Abstract In recent years, many few‐shot classification methods have been proposed. However, only a few of them have explored robust classification, which is an important aspect of human visual intelligence. Humans can effortlessly recognise visual patterns, including lines, circles, and even characters, from image data that has been corrupted or degraded. In this paper, the authors investigate a robust classification method that extends the classical paradigm of robust geometric model fitting. The method views an image as a set of points in a low‐dimensional space and analyses each image through low‐dimensional geometric model fitting. In contrast, the majority of other methods, such as deep learning methods, treat an image as a single point in a high‐dimensional space. The authors evaluate the performance of the method using a noisy Omniglot dataset. The experimental results demonstrate that the proposed method is significantly more robust than other methods. The source code and data for this paper are available at https://github.com/pengsuhua/PMF_OMNIGLOT .

Due to the difference in high-rise distance, laser incident angle, and occlusion, incomplete point cloud of building facades is incomplete. The existing point cloud facade completion work does not fully explore the semantic constraints. Thus, it is difficult to deal with the facades with large-area missing. This paper proposes a novel facade completion framework for point clouds. By introducing semantic constraints and semantic-synchronized GAN, we solve the facade completion problem with large-area missing while the semantic consistency is augmented.

The point clouds acquired by a vehicle-borne mobile laser scanning (MLS) system have shown great potential for many applications such as intelligent transportation systems, road infrastructure inventories, and high-definition (HD) maps to support the advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). This paper presents a novel two-step approach to automated detection and reconstruction of three-dimensional (3D) highway curves from MLS point clouds. However, when dealing with noisy, unstructured, dense point clouds, we often face some challenges, most notably in handling of the outliers introduced during road marking detection and in recognition of curve types during 3D curve reconstruction. Our approach is formed by two main algorithms: a detector based on intensity variance and a robust model fitting estimator. The experimental results obtained using both a virtual scan dataset and a real MLS dataset demonstrated that our approach is very promising in handling of the outliers and reconstruction of 3D road curves. Specifically, a relative accuracy of 0.6% has been achieved in estimation of circle radii based on the virtual scan dataset. A comparative study also showed that our road marking detection approach is more effective and more stable than state-of-the-art approaches.

This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations.

This paper presents a novel method for reconstruction of three-dimensional (3D) zebra crossings from mobile laser scanning (MLS) point clouds. Firstly, we extract the zebra crossings from the 3D point cloud data in data preprocessing. Secondly, the fitting model is generated by seven parameters to determine one plane commonly and then calculating similarity for fitting the zebra crossings point clouds. Finally, the cuckoo search algorithm is used to adjust the parameters and optimize the results to obtain the optimal model and the geometric information of the zebra crossings can be acquired simultaneously. The proposed algorithm is tested on a set of point-clouds acquired by a RIEGL VMX-450 LiDAR system. The experimental results show the feasibility and stability of our method and the zebra crossings of urban road can be automatically and effectively reconstructed.

LIDAR (Light Detection and Ranging) is an especially effective tool for acquiring geo-referenced point clouds of urban site. Accurate extraction of elevated features such as building rooftops is vitally important in various applications. However, it is still challenging to determine an accurate rooftop contour from the irregularly distributed LIDAR point clouds. In this paper an efficient LIDAR segmentation method is presented in order to achieve automated rooftop extraction. First, we apply a voxel-based upward growing algorithm that filters out the ground points from the raw point cloud scenes. Second, we employ a Euclidean based clustering method on non-ground points by making use of nearest neighbors. Then we introduce RANSAC (RANdom SAmple Consensus) technique to estimate primitive planes for fitting rooftop facets. Finally, we use concave hull and L0regularization to determine the rooftop contour. Accurate experimental results demonstrate the validity of our segmentation method for rooftop extraction.

Traditional road surveying methods rely largely on in-situ measurements, which are time consuming and labor intensive. Recent Mobile Laser Scanning (MLS) techniques enable collection of road data at a normal driving speed. However, extracting required information from collected MLS data remains a challenging task. This paper focuses on examining the current status of automated on-road object extraction techniques from 3D MLS points over the last five years. Several kinds of on-road objects are included in this paper: curbs and road surfaces, road markings, pavement cracks, as well as manhole and sewer well covers. We evaluate the extraction techniques according to their method design, degree of automation, precision, and computational efficiency. Given the large volume of MLS data, to date most MLS object extraction techniques are aiming to improve their precision and efficiency.