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1. Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops

   https://doi.org/10.3390/rs13193975  

   Zhang, Fei; Hassanzadeh, Amirhossein; Kikkert, Julie; Pethybridge, Sarah Jane; van Aardt, Jan (October 2021, Remote Sensing)

   The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds, collected concurrently in five UAS flights experimental fields of a short crop (snap bean), in order to explore how well the SfM approach performs compared with LiDAR for crop phenotyping. The main methods include calculating the cloud-to-mesh distance (C2M) maps between the preprocessed point clouds, as well as computing a multiscale model-to-model cloud comparison (M3C2) distance maps between the derived digital elevation models (DEMs) and crop height models (CHMs). We also evaluated the crop height and the row width from the CHMs and compared them with field measurements for one of the data sets. Both SfM and LiDAR point clouds achieved an average RMSE of ~0.02 m for crop height and an average RMSE of ~0.05 m for row width. The qualitative and quantitative analyses provided proof that the SfM approach is comparable to LiDAR under the same UAS flight settings. However, its altimetric accuracy largely relied on the number and distribution of the ground control points. 

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2. A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

   https://doi.org/10.1145/3206025.3206080  

   Yue, Xiangyu; Wu, Bichen; Seshia, Sanjit A.; Keutzer, Kurt; Sangiovanni-Vincentelli, Alberto L. (June 2018, ICMR '18 Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval)

   3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate pointlevel labels from a computer game. To our best knowledge, this is the first publication on LiDAR point cloud simulation framework for autonomous driving. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic registration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed. 

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3. A PARALLEL ALGORITHM FOR LOCAL POINT DENSITY INDEX COMPUTATION OF LARGE POINT CLOUDS

   https://doi.org/10.5194/isprs-annals-VIII-4-W2-2021-75-2021  

   Vo, A. V.; Lokugam Hewage, C. N.; Le Khac, N. A.; Bertolotto, M.; Laefer, D. (January 2021, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   Abstract. Point density is an important property that dictates the usability of a point cloud data set. This paper introduces an efficient, scalable, parallel algorithm for computing the local point density index, a sophisticated point cloud density metric. Computing the local point density index is non-trivial, because this computation involves a neighbour search that is required for each, individual point in the potentially large, input point cloud. Most existing algorithms and software are incapable of computing point density at scale. Therefore, the algorithm introduced in this paper aims to address both the needed computational efficiency and scalability for considering this factor in large, modern point clouds such as those collected in national or regional scans. The proposed algorithm is composed of two stages. In stage 1, a point-level, parallel processing step is performed to partition an unstructured input point cloud into partially overlapping, buffered tiles. A buffer is provided around each tile so that the data partitioning does not introduce spatial discontinuity into the final results. In stage 2, the buffered tiles are distributed to different processors for computing the local point density index in parallel. That tile-level parallel processing step is performed using a conventional algorithm with an R-tree data structure. While straight-forward, the proposed algorithm is efficient and particularly suitable for processing large point clouds. Experiments conducted using a 1.4 billion point data set acquired over part of Dublin, Ireland demonstrated an efficiency factor of up to 14.8/16. More specifically, the computational time was reduced by 14.8 times when the number of processes (i.e. executors) increased by 16 times. Computing the local point density index for the 1.4 billion point data set took just over 5 minutes with 16 executors and 8 cores per executor. The reduction in computational time was nearly 70 times compared to the 6 hours required without parallelism. 

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4. Polylidar3D-Fast Polygon Extraction from 3D Data

   https://doi.org/10.3390/s20174819  

   Castagno, Jeremy; Atkins, Ella (September 2020, Sensors)

   null (Ed.)

   Flat surfaces captured by 3D point clouds are often used for localization, mapping, and modeling. Dense point cloud processing has high computation and memory costs making low-dimensional representations of flat surfaces such as polygons desirable. We present Polylidar3D, a non-convex polygon extraction algorithm which takes as input unorganized 3D point clouds (e.g., LiDAR data), organized point clouds (e.g., range images), or user-provided meshes. Non-convex polygons represent flat surfaces in an environment with interior cutouts representing obstacles or holes. The Polylidar3D front-end transforms input data into a half-edge triangular mesh. This representation provides a common level of abstraction for subsequent back-end processing. The Polylidar3D back-end is composed of four core algorithms: mesh smoothing, dominant plane normal estimation, planar segment extraction, and finally polygon extraction. Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. We demonstrate Polylidar3D’s versatility and speed with real-world datasets including aerial LiDAR point clouds for rooftop mapping, autonomous driving LiDAR point clouds for road surface detection, and RGBD cameras for indoor floor/wall detection. We also evaluate Polylidar3D on a challenging planar segmentation benchmark dataset. Results consistently show excellent speed and accuracy. 

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5. Super-Resolution 3D Laser Scanning Based on Interval Arithmetic

   https://doi.org/10.1109/TIM.2020.2987619  

   Walecki, Peter; Taubin, Gabriel (April 2020, IEEE Transactions on Instrumentation and Measurement)

   Most 3D laser scanners are based on 3D optical triangulation algorithms, where the location of each 3D point is estimated as the intersection of a camera ray and a plane of light projected by a laser line generator. Since a physical laser line generator projects a sheet of light of finite thickness, inaccurate measurement and errors result from assuming that the plane of light is infinitesimally thin. We propose a new mathematical formulation for 3D optical triangulation based on interval arithmetic, where 3D points are only determined within certain bounds along the camera rays, and multiple measurements are used to tighten these bounds. We propose the Line Segment Cloud as an alternative surface representation to visualize the measurement errors within the proposed framework. We introduce the Iterative Line Segment Tightening algorithm to convert line segment clouds to point clouds, as a preprocessing step prior to surface reconstruction. We describe how to construct a low cost laser line 3D scanner, where the camera is fixed with respect to the object and the laser line generator is mounted on a high resolution motion platform. We describe a GPU-based implementation where the large number of captured images are processed in real time. Finally, we present some experimental results. 

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