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1. 哈尔滨工程大学 智能科学与工程学院, 黑龙江 哈尔滨 150009
2. 哈尔滨第一机械集团有限公司, 黑龙江 哈尔滨 150000
Received:04 September 2023,
Published Online:15 January 2024,
Published:30 December 2023
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Jingqi MA, Qiwen YU, Ping HUANG, et al. A Real-time Compression Algorithm of Color Point Cloud Streams for Environmental Scanning[J]. Acta Armamentarii, 2023, 44(S2): 167-177.
Jingqi MA, Qiwen YU, Ping HUANG, et al. A Real-time Compression Algorithm of Color Point Cloud Streams for Environmental Scanning[J]. Acta Armamentarii, 2023, 44(S2): 167-177. DOI: 10.12382/bgxb.2023.0856.
随着数字孪生技术的不断发展
人们面临将大规模、高维度的点云数据进行压缩编码的问题
以提高传输效率。然而大多点云编码方式存在压缩实时性不强、压缩效率低、点云格式要求过高等问题。针对解决这些问题
提出了一种基于谷歌Draco几何压缩库的实时彩色点云流压缩(Real-time Color Stream Draco
RCS-Draco)算法。将算法集成到ROS框架内
借助ROS消息流
对点云流实时地进行编码和解码
提高了算法的实时性;通过建立优化裁剪模型
对点云进行裁剪和滤波
去除了漂移和离群点云
提高了压缩算法的压缩效率。建立量化预测模型
对点云的RGB颜色信息进行编码
解决了大多数点云压缩算法无法处理颜色信息的问题。对比试验通过调整压缩等级和量化参数
证明RCS-Draco算法的平均压缩率最高能够达到77%、平均压缩和解压时间小于0.035s、位置平均误差小于0.05m、属性平均误差小于35;并通过相融试验证明RCS-Draco算法在各项指标上优于Draco算法。试验结果表明
RCS-Draco压缩算法在压缩实时性、效率、点云格式方面均表现良好
能够有效地提升传输效率。
The problems of compressing and encoding the large-scale and high-dimensional point cloud data to improve transmission efficiency arise with the continuous development of digital twin technology. However
most point cloud coding methods have weak real-time compression
low compression efficiency
and high requirement of point cloud format. A real-time color stream Draco (RCS-Draco) compression algorithm based on Google Draco geometric compression library is proposed to solve these problems. By integrating the algorithm into the ROS framework
the point cloud stream is encoded and decoded in real time by means of ROS message flow
which improves the real-time performance of the algorithm. An optimal clipping model is established to clip and filter the point cloud
and remove the drift and outlier point cloud
thus improving the compression efficiency of the algorithm. The RGB color information of point cloud is encoded by establishing a quantitative prediction model
which solves the problem that most point cloud compression algorithms cannot process color information. By adjusting the compression grade and quantization parameters
it is proved that the average compression rate of RCS-Draco algorithm can reach 77%
the average compression and decompression time is less than 0.035s
the average position error is less than 0.05m
and the average attribute error is less than 35. The RCS-Draco algorithm is superior to Draco algorithm in every index through the integration test. The experimental results show that the RCS-Draco compression algorithm performs well in terms of real-time compression
efficiency and point cloud format
and can effectively improve transmission efficiency.
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WANG X , GAO L , WEI Y Y , et al. Research on parallel experiment based on the simulation platform of mission-engagement level system [J ] Acta Armamentarii , 2022 , 43 ( S1 ): 183 - 188 . (in Chinese) DOI: 10.12382/bgxb.2022.A015 http://doi.org/10.12382/bgxb.2022.A015 A method of heterogeneous interconnection between mission-level and engagement-level simulation platforms is explored to meet the requirements of large scene and refinement of system simulation. In the heterogeneous interconnection method for the system simulation,the functional layer middlewares are constructed based on the overall architecture of the existing simulation platform,and the data processing flow is designed data and the interaction service is formed through Socket communication.The overall function is designed according to business needs,the simulation data is collected and transmitted synchronously.The simulation experiments were designed,and the experimental results were analyzed to verify the feasibility of the proposed method.The mission level platform provides large scene action commands for the engagement level platform,and the engagement level platform provides the fine simulation of small scenes for the mission level platform. The design method could meet the requirements of brigade-level simulation under the background of all-dimensional and multi-angle joint operations.
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JI G , HAO J G , ZHANG Z W . Design scheme of virtual twin system for UAV combat [J ] . Acta Armamentarii , 2022 , 43 ( 8 ): 1902 - 1912 . (in Chinese) DOI: 10.12382/bgxb.2021.0408 http://doi.org/10.12382/bgxb.2021.0408 To deal with the low precision of the UAV model in the process of combat simulation, the difficulty of virtualreality interactive operation, and the weak combat experience of commanders, the design scheme of the combat simulation system is proposed based on virtual twin technology, which is used for synchronous operation of actual UAV combat and simulation/deduction, and outputting intelligent assisted decision-making according to the battlefield situation. On the basis of digital twin, the connotation of virtual twin technology is proposed. Combined with the advanced means of artificial intelligence, the framework of virtual twin system for UAV combat is designed. Finally, taking the combat process of individual combat quadrotor UAV as an example, the hardware and software architecture is designed, and the operation process is analyzed. The case study findings shows that the model under the virtual twin system runs more accurately, and can realize the virtual-reality synchronization, interactive operation, visual interface and intelligent decision-making real-time output, so that the commander's sense of participation is enhanced, and the combat command efficiency is improved.
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SHAO Y B , LIU J , LONG H , et al. Voice transmission and reconstruction on the battlefield [J ] . Acta Armamentarii , 2022 , 43 ( 11 ): 2827 - 2835 . (in Chinese) DOI: 10.12382/bgxb.2021.0549 http://doi.org/10.12382/bgxb.2021.0549 A spectrogram-based reconstruction method is proposed to address the problem of poor voice transmission and reconstruction quality under conditions of high compression ratios and low signal-to-noise ratios. Speech signals are converted into spectrograms at the transmitter, which are later transmitted and denoised at the receiver. Then, the amplitude spectrum is restored from the denoised spectrogram image and the voice is reconstructed through the amplitude spectrum by the voice model. Experiments show that the perceptual evaluation of speech quality (PESQ) of the reconstructed speech exceeds 3 under noise-free environment with compression ratios of 10 and 40 respectively. The PESQ can also exceed 2 under the low signal-to-noise ratio with compression ratio of 10. The proposed method shows significant improvement in reconstructed speech quality at high compression ratios compared with the traditional algorithm.
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ZHOU K X , DING X H , LÜ D D , et al. Research on LDPC decoding algorithm with high efficiency and low complexity for space-based information transmission [J ] . Acta Armamentarii , 2023 (2023-04-10)[2023-04-10 ] . http://www.co-journal.com/CN/10.12382/bgxb.2022.1044 http://www.co-journal.com/CN/10.12382/bgxb.2022.1044 http://www.co-journal.com/CN/10.12382/bgxb.2022.1044. (in Chinese)
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SUN X B MAH , LIU M . A novel point cloud compression algorithm based on clustering [J ] . IEEE Robotics and Automation Letters , 2019 , 4 ( 2 ): 2132 - 2139 . DOI: 10.1109/LRA.2019.2900747 http://doi.org/10.1109/LRA.2019.2900747 Due to the enormous volume of point cloud data, transmitting and storing the data requires large bandwidth and storage space. It could be a critical bottleneck, especially in tasks such as autonomous driving. In this letter, we propose a novel point cloud compression algorithm based on clustering. The proposed scheme starts with a range image-based segmentation step, which segments the three-dimensional (3-D) range data into ground and main objects. Then, it introduces a novel prediction method according to the segmented regions' shape. This prediction method is inspired by the depth modeling modes used in 3-D high-efficiency video coding for depth map coding. Finally, the few prediction residual is efficiently compressed with several lossless or lossy data compression techniques. Experimental results show that the proposed algorithm can largely eliminate the spatial redundant information of the point cloud data. The lossless compression scheme reaches a compression ratio of nearly 5%, which means that the point cloud is compressed to 5% of its original size without any distance distortion. Compared with other methods, the proposed compression algorithm also shows better performance.
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黄源 , 达飞鹏 , 唐林 . 基于改进八叉树的三维点云压缩算法 [J ] . 光学学报 , 2017 , 37 ( 12 ): 1210003 .
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WANG Q , JIANG LY , SUN XB , et al. An efficient LiDAR point cloud map coding scheme based on segmentation and frame-inserting network [J ] . Sensors , 2022 , 22 ( 14 ): 5108 . DOI: 10.3390/s22145108 http://doi.org/10.3390/s22145108 https://www.mdpi.com/1424-8220/22/14/5108 https://www.mdpi.com/1424-8220/22/14/5108 In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point cloud map consists of numerous single scans spliced together, by recording the time stamp and quaternion matrix of each scan during map building, we cast the point cloud map compression into the point cloud sequence compression problem. The coding architecture includes two techniques: intra-coding and inter-coding. For intra-frames, a segmentation-based intra-prediction technique is developed. For inter-frames, an interpolation-based inter-frame coding network is explored to remove temporal redundancy by generating virtual point clouds based on the decoded frames. We only need to code the difference between the original LiDAR data and the intra/inter-predicted point cloud data. The point cloud map can be reconstructed according to the decoded point cloud sequence and quaternion matrices. Experiments on the KITTI dataset show that the proposed coding scheme can largely eliminate the temporal and spatial redundancies. The point cloud map can be encoded to 1/24 of its original size with 2 mm-level precision. Our algorithm also obtains better coding performance compared with the octree and Google Draco algorithms.
CHRISTENSEN A , LEHOTTSKY D , POULSEN M K , et al. Presenting a novel pipeline for performance comparison of V-PCC and G-PCC point cloud compression methods on datasets with varying properties [C ] // Proceedings of the 17th International Joint Conference on Computer Vision, Imagingand Computer Graphics Theory and Applications.Red Hook,NY , US : Curran Associates ,Inc., 2022 : 387 - 393 .
曾志贤 , 曹建军 , 翁年凤 , 等 . 结合关键帧提取的视频-文本跨模态实体分辨双重编码方法 [J ] . 兵工学报 , 2022 , 43 ( 5 ): 1107 - 1116 . DOI: 10.12382/bgxb.2021.0262 http://doi.org/10.12382/bgxb.2021.0262 现有的视频-文本跨模态实体分辨方法在视频处理上均采用均匀取帧的方法,必然导致视频信息的丢失,增加问题的复杂度。针对这一问题,提出一种结合关键帧提取的视频-文本跨模态实体分辨双重编码方法(DEIKFE)。以充分保留视频信息表征为前提,设计关键帧提取算法提取视频中的关键帧,获得视频关键帧集合表示。对于视频关键帧集合和文本,采用多级编码的方法,分别提取表征视频和文本的全局、局部和时序的特征,将其进行拼接形成多级编码表示。将该编码表示映射至共同嵌入空间,采用强负样本跨模态三元组损失对模型参数进行优化,使得匹配的视频-文本相似度越大,而不匹配的视频-文本相似度越小。通过在MSR-VTT、VATEX两个数据集上进行实验验证,与现有方法进行对比,在总体性能R@sum上分别提升了9.22%、2.86%,证明了该方法的优越性。
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LUO G L , HE B Q , XIONG Y B , et al. An optimized convolutional neural network for the 3D point-cloud compression [J ] . Sensors , 2023 , 23 ( 4 ): 2250 . DOI: 10.3390/s23042250 http://doi.org/10.3390/s23042250 https://www.mdpi.com/1424-8220/23/4/2250 https://www.mdpi.com/1424-8220/23/4/2250 Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.
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律帅 , 达飞鹏 , 黄源 . 基于数据类型转换的点云快速有损压缩算法 [J ] . 图学学报 , 2016 , 37 ( 2 ): 199 - 205 . DOI: 10.11996/JG.j.2095-302X.2016020199 http://doi.org/10.11996/JG.j.2095-302X.2016020199 针对海量三维点云数据为计算机存储和传输增加沉重负担的问题,提出一种基于数据类型转换的点云快速有损压缩算法。首先设计出一种数据类型转化规则-FtoI 规则,根据FtoI规则将浮点数类型点云转换成整数类型点云,然后将整数类型点云切分成许多小单元面块,每一单元点云生成最小生成树,按广度优先的顺序对树形结构进行编码。同时,按照树形结构对父子节点的差值进行编码,把整型差值分成两部分编码,符号一部分,其绝对值一部分,其中绝对值部分采用算术编码进行压缩。实验表明该文算法在保证整个三维点云模型的质量情况下,具有不错的压缩速度和压缩率。
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