The semantic labeling of the urban area is an essential but challenging task for a wide variety of applications such as mapping, navigation, and monitoring. The rapid advance in Light Detection and Ranging (LiDAR) systems provides this task with a possible solution using 3D point clouds, which are accessible, affordable, accurate, and applicable. Among all types of platforms, the airborne platform with LiDAR can serve as an efficient and effective tool for large-scale 3D mapping in the urban area. Against this background, a large number of algorithms and methods have been developed to fully explore the potential of 3D point clouds. However, the creation of publicly accessible large-scale annotated datasets, which are critical for assessing the performance of the developed algorithms and methods, is still at an early age. In this work, we present a large-scale aerial LiDAR point cloud dataset acquired in a highly-dense and complex urban area for the evaluation of semantic labeling methods. This dataset covers an urban area with highly-dense buildings of approximately 1 km² and includes more than three million points with five classes of objects labeled. Moreover, experiments are carried out with the results from several baseline methods, demonstrating the feasibility and capability of the dataset serving as a benchmark for assessing semantic labeling methods.

In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360 $°$ laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation.

During the Orbital B Phase mission of the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx), the OSIRIS-REx’s Laser Altimeter (OLA) instrument scanned asteroid Bennu and obtained hundreds of point cloud frames, making a meticulous 3D surface modeling of Bennu realizable. However, these acquired point clouds suffer from problems like inaccurate pose information, low overlap ratios, uneven densities, and outliers. To create a precise 3D shape model via topographical mapping using OLA data, we present an optimized connection graph guided global bundle block adjustment method with variable weights capable of constructing a complete 3D model of the asteroid via a straightforward global adjustment of offsets. Specifically, by using an improved Fast Double-channel Aggregated Feature Transform, 3D keypoint extraction and their correspondences can be determined with a sub-pixel level of matching accuracy. Then, a graph of the shortest path algorithm is constructed to establish the optimized connections of all the OLA point clouds. Afterward, the global bundle block adjustment method with variable weights is introduced to minimize the keypoint matches simultaneously, reducing the offsets between the overlapping point clouds. Finally, the shape model of asteroid Bennu was constructed using the finely adjusted point clouds, with RMSE as small as 0.0743 m compared with the reference shape model, reaching approximately the limit of the instrument range errors (i.e., 3 cm ) of observation data. Results of the case study on asteroid Bennu demonstrated that the proposed method could improve the accuracy of point cloud registration and meet the application requirements.

Matching multi-modal remote sensing images (MRSI) is a challenging task. Due to significant nonlinear radiation differences (NRD), traditional image matching methods cannot achieve satisfactory results. Current research shows that structural information can get more robust matching results compared with texture information (i.e., gradient features) from images. In order to better explore the structural information of images, this paper proposes a MRSIs matching method using structure saliency features, called weighted structure saliency feature (WSSF). Two strategies are investigated and integrated in WSSF to improve the matching performance. The scale space is constructed based on the pointwise shape-adaptive texture scale filtering, which can better retain the structure features, and the second-order Gaussian steerable filtering, edge confidence map and phase features are combined to establish the structural saliency map combined with second-order Gaussian steerable filtering, which is much more robust to NRD than traditional gradient map. The performance of the proposed method was evaluated on a total of 120 image pairs from two MRSI datasets, and compared with state-of-the-art matching methods including the histogram of the orientation of weighted phase (HOWP), locally normalized image feature transform (LNIFT), co-occurrence filter space matching (CoFSM), radiation-variation insensitive feature transform (RIFT), local phase sharpness orientation (LPSO), and position-scale-orientation SIFT (PSO-SIFT). Experimental results indicate that WSSF obtains satisfactory and reliable results in terms of success rate and matching accuracy. Compared with the above six methods, the matching accuracy of WSSF is improved by more than 20.275%, and the success rate is improved by over 5.833%. The source code will be publicly available in https://github.com/WGY-RS/WSSF.