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Determination of the potential of solar energy, solar plant design, and grid integration based on lidar data processing in northern border region

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JOURNAL OF OPTICS-INDIA 2024年第3期53卷 2142-2150页

作者： Azzahrani, Ahmad Salih Kadhim, Ahmed Chyad Northern Border Univ Dept Elect Engn ArarKSA Saudi Arabia Univ Technol Iraq Dept Laser & Optoelectron Engn Baghdad Iraq

This proposal introduces advancements in the state-of-the-art solar energy industry by using lidar-based location characterization. The distribution of solar energy across the northern border region is highlighted using lidar data processing. This methodology is proposed to scientifically derive the tilt and azimuth angles of the target location and to quantify solar direct, diffuse, and global horizontal irradiance for regions of interest. The cloud points of lidar give a new concept about distribution of solar panels and their orientation using tilt and azimuth angles. This method is strategically optimized the use and adoption of solar incentives according to the grid age and its vulnerabilities to solar variability in the region. The outcomes will enable the formulation of typical meteorological data, measured or forecasted time series of irradiances over distributed assets.

关键词： Renewable energy Solar energy lidar data processing

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CPU and GPU oriented optimizations for lidar data processing

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JOURNAL OF COMPUTATIONAL SCIENCE 2024年 79卷

作者： Munoz, Felipe Asenjo, Rafael Navarro, Angeles Cabaleiro, J. Carlos Univ Malaga Dept Comp Architecture Bulevar Louis Pasteur 35 Malaga Spain Univ Santiago De Compostela Ctr Singular Invest Tecnol Intelixentes CiTIUS Dept Elect & Comp Santiago De Compostela Spain

Digital Terrain Models (DTM) can be accurately obtained from clouds of lidar points but the corresponding cloud processing time can be prohibitive. This paper describes several optimization techniques that have been applied to the Overlap Window Method (OWM) that is a key component in DTM applications. OWM was originally implemented in R which translates into serious limitations in terms of the size of the lidar point cloud that can be processed. We have ported the code to C++, significantly optimized the data structure to minimize memory accesses, and developed parallel implementations for CPU and GPU commodity devices using oneAPI libraries and tools. This results in CPU and GPU versions that are up to 19x and 83x faster, respectively, than an OpenMP baseline that uses eight CPU cores. Most importantly, the proposed optimizations for CPU and GPU can be paramount to get the most out of other lidar-based algorithms in which the careful selection of the right data structure, parallelization strategies and memory access reduction techniques will certainly result in significant performance improvements.

关键词： lidar data processing Digital Terrain Model Tree data structures Parallel optimization GPU SYCL CUDA oneAPI

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Advancing Trans-Domain Classification With Knowledge Distillation: Bridging lidar and Image data

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IEEE ACCESS 2025年 13卷 20574-20583页

作者： Ortiz, Jesus Eduardo Creixell, Werner Univ Tecn Federico Santa Maria Dept Elect Valparaiso 2390123 Chile

Recent advancements in deep learning have significantly improved image classification models, yet extending these models to alternative data forms, such as point clouds from Light Detection and Ranging (lidar) sensors, presents considerable challenges. This paper explores applying knowledge distillation techniques as a solution, aiming to transfer the learned competencies from established image classification frameworks to point cloud classification tasks. Our methodology involves distilling complex model insights into more computationally efficient forms suitable for lidar data, thus enabling substantial resource savings without sacrificing performance. Experimental evaluations across various benchmark datasets demonstrate that our distilled models not only rival their original counterparts in accuracy but also surpass conventional point cloud classification methods in both efficiency and scalability. Additionally, we delve into the impact of varying distillation techniques on model adaptability and performance within the lidar domain. The findings underscore the utility of knowledge distillation in enhancing the trans-domain applicability of image classification models, potentially revolutionizing their deployment across diverse data types.

关键词： Computational modeling Adaptation models data models Laser radar Biological system modeling Transformers Sensors Point cloud compression Knowledge engineering Image classification Knowledge distillation trans-domain classification point cloud classification lidar data processing deep learning model compression sensor fusion efficient machine learning autonomous systems cross-domain machine learning

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A lightweight depth completion network with spatial efficient fusion

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IMAGE AND VISION COMPUTING 2025年 153卷

作者： Fu, Zhichao Wu, Anran Zhuang, Zisong Wu, Xingjiao He, Jun East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Fudan Univ Shanghai Peoples R China

Depth completion is a low-level task rebuilding the dense depth from a sparse set of measurements from lidar sensors and corresponding RGB images. Current state-of-the-art depth completion methods used complicated network designs with much computational cost increase, which is incompatible with the realistic-scenario limited computational environment. In this paper, we explore a lightweight and efficient depth completion model named Light-SEF. Light-SEF is a two-stage framework that introduces local fusion and global fusion modules to extract and fuse local and global information in the sparse lidar data and RGB images. We also propose a unit convolutional structure named spatial efficient block (SEB), which has a lightweight design and extracts spatial features efficiently. As the unit block of the whole network, SEB is much more cost-efficient compared to the baseline design. Experimental results on the KITTI benchmark demonstrate that our LightSEF achieves significant declines in computational cost (about 53% parameters, 50% FLOPs & MACs, and 36% running time) while showing competitive results compared to state-of-the-art methods.

关键词： Depth completion lidar data processing Lightweight network Spatial efficient Multi-modal fusion

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3DGTN: 3-D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1-1页

作者： Lu, Dening Gao, Kyle Xie, Qian Xu, Linlin Li, Jonathan Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Univ Oxford Dept Comp Sci Oxford OX1 3QD England Univ Waterloo Dept Syst Design Engn Waterloo N2L 3G1 ON Canada Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada

Although the application of Transformers to 3-D point cloud processing has achieved significant progress and success, it is still challenging for existing 3-D Transformer methods to efficiently and accurately learn both valuable global and local features for improved applications. This article presents a novel point cloud representational learning network, called 3-D Dual Self-attention global local (GLocal) Transformer Network (3DGTN), for improved feature learning in both classification and segmentation tasks, with the following key contributions. First, a GLocal feature learning (GFL) block with the dual self-attention mechanism [i.e., a novel point-patch self-attention, called PPSA, and a channel-wise self-attention (CSA)] is designed to efficiently learn the global and local context information. Second, the GFL block is integrated with a multiscale Graph Convolution-based local feature aggregation (LFA) block, leading to a GLocal information extraction module that can efficiently capture critical information. Third, a series of GLocal modules are used to construct a new hierarchical encoder-decoder structure to enable the learning of information in different scales in a hierarchical manner. The proposed framework is evaluated on both classification and segmentation datasets, demonstrating that the proposed method is capable of outperforming many state-of-the-art methods on both synthetic and lidar data. Our code has been released at https://***/d62lu/3DGTN.

关键词： Point cloud compression Feature extraction Three-dimensional displays Decoding Task analysis Representation learning Graph convolution lidar data processing point cloud classification point cloud segmentation self-attention mechanism transformer

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Self-Supervised Depth Completion Based on Multi-Modal Spatio-Temporal Consistency

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REMOTE SENSING 2023年第1期15卷 135-135页

作者： Zhang, Quan Chen, Xiaoyu Wang, Xingguo Han, Jing Zhang, Yi Yue, Jiang Nanjing Univ Sci & Technol Sch Elect Engn & Optoelect Technol Nanjing 210000 Peoples R China Hohai Univ Coll Sci Nanjing 210000 Peoples R China

Due to the low cost and easy deployment, self-supervised depth completion has been widely studied in recent years. In this work, a self-supervised depth completion method is designed based on multi-modal spatio-temporal consistency (MSC). The self-supervised depth completion nowadays faces other problems: moving objects, occluded/dark light/low texture parts, long-distance completion, and cross-modal fusion. In the face of these problems, the most critical novelty of this work lies in that the self-supervised mechanism is designed to train the depth completion network by MSC constraint. It not only makes better use of depth-temporal data, but also plays the advantage of photometric-temporal constraint. With the self-supervised mechanism of MSC constraint, the overall system outperforms many other self-supervised networks, even exceeding partially supervised networks.

关键词： depth completion lidar data processing self-supervised sensor fusion multi-modal deep learning

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3DLST: 3D Learnable Supertoken Transformer for lidar point cloud scene segmentation

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2025年 140卷

作者： Lu, Dening Xu, Linlin Zhou, Jun Gao, Kyle (Yilin) Li, Jonathan Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Univ Calgary Dept Geomatics Engn Calgary AB T2N 1N4 Canada Hong Kong Polytech Univ Sch Nursing TU428 Hong Kong Peoples R China Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada

3D Transformers have achieved great success in point cloud understanding and representation. However, there is still considerable scope for further development in effective and efficient Transformers for large-scale lidar point cloud scene segmentation. This paper proposes a novel 3D Transformer framework, named 3D Learnable Supertoken Transformer (3DLST). The key contributions are summarized as follows. Firstly, we introduce the first Dynamic Supertoken Optimization (DSO) block for efficient token clustering and aggregating, where the learnable supertoken definition avoids the time-consuming pre-processing of traditional superpoint generation. Since the learnable supertokens can be dynamically optimized by multi-level deep features during network learning, they are tailored to the semantic homogeneity-aware token clustering. Secondly, an efficient Cross-Attention-guided Upsampling (CAU) block is proposed for token reconstruction from optimized supertokens. Thirdly, the 3DLST is equipped with a novel W-net architecture instead of the common U-net design, which is more suitable for Transformer-based feature learning. The SOTA performance on challenging lidar datasets (airborne MultiSpectral lidar (MS-lidar) (89.3% of the average F1 score) and DALES (80.2% of mIoU)) demonstrate the superiority of 3DLST. Furthermore, 3DLST also achieves satisfactory results in terms of algorithm efficiency, which is up to 5x faster than previous best-performing methods.

关键词： Transformer Supertoken lidar data processing Point cloud segmentation Cross-attention mechanism

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An Efficient Information-Reinforced lidar Deep Completion Network without RGB Guided

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REMOTE SENSING 2022年第19期14卷 4689-4689页

作者： Wei, Ming Zhu, Ming Zhang, Yaoyuan Sun, Jiaqi Wang, Jiarong Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Due to the sparsity of point clouds obtained by lidar, the depth information is usually not complete and dense. The depth completion task is to recover dense depth information from sparse depth information. However, most of the current deep completion networks use RGB images as guidance, which are more like a processing method of information fusion. They are not valid when there is only sparse depth data and no other color information. Therefore, this paper proposes an information-reinforced completion network for a single sparse depth input. We use a multi-resolution dense progressive fusion structure to maximize the multi-scale information and optimize the global situation by point folding. At the same time, we re-aggregate the confidence and impose another depth constraint on the pixel depth to make the depth estimation closer to the ground trues. Our experimental results on KITTI and NYU Depth v2 datasets show that the proposed network achieves better results than other unguided deep completion methods. And it is excellent in both accuracy and real-time performance.

关键词： depth completion lidar data processing image processing deep learning

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Learning and Recognizing Archeological Features from lidar data

Learning and Recognizing Archeological Features from LiDAR D...

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IEEE International Conference on Big data (Big data)

作者： Albrecht, Conrad M. Fisher, Chris Freitag, Marcus Hamann, Hendrik F. Pankanti, Sharathchandra Pezzutti, Florencia Rossi, Francesca IBM Res TJ Watson Res Ctr Yorktown Hts NY 10598 USA Colorado State Univ Dept Antropol & Geog Ft Collins CO 80523 USA

ISBN: (纸本)9781728108582

We present a remote sensing pipeline that processes lidar (Light Detection And Ranging) data through machine & deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e.g. IBM PAIRS Geoscape [2]. Today, archeologists get overwhelmed by the task of visually surveying huge amounts of (raw) lidar data in order to identify areas of interest for inspection on the ground. We showcase a software system pipeline that results in significant savings in terms of expert productivity while missing only a small fraction of the artifacts. Our work employs artificial neural networks in conjunction with an efficient spatial segmentation procedure based on domain knowledge. data processing is constraint by a limited amount of training labels and noisy lidar signals due to vegetation cover and decay of ancient structures. We aim at identifying geo-spatial areas with archeological artifacts in a supervised fashion allowing the domain expert to flexibly tune parameters based on her needs.

关键词： lidar data processing machine learning archeology remote sensing applications

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lidar SLAM Positioning Quality Evaluation in Urban Road Traffic 3rd

LiDAR SLAM Positioning Quality Evaluation in Urban Road Traf...

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3rd European-Alliance-for-Innovation (EAI) International Conference on Intelligent Transport Systems (INTSYS)

作者： Andert, Franz Mosebach, Henning German Aerosp Ctr DLR Inst Transportat Syst Berlin Germany

ISBN: (纸本)9783030388225;9783030388218

This paper addresses the positioning quality of Simultaneous Localization And Mapping (SLAM) based on Light Detection and Ranging (lidar) sensors within urban road traffic. Based on the assumption of functional capability of existing SLAM implementations, the paper evaluates specific details of urban car drives that arise when SLAM is to be used for automatic car control. In the presented case, lidar-based positioning is done with the Google Cartographer software which generates real-time updates that are compared to GNSS reference. The evaluation is done by using own Light Detection And Ranging (lidar) sensor recordings from urban driving. Next to the overall GNSS-free path estimation, the paper zooms into some typical situations (e.g. waiting at busy intersection, driving curves) where SLAM might be inaccurate.

关键词： GPS-free navigation SLAM lidar data processing

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