Query：
学者姓名：郑明魁
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
存储和传输激光雷达点云数据对于许多自动驾驶应用来说是必不可少的。由于激光雷达点云数据的稀疏性和无序性，很难将激光雷达点云数据压缩到较小的体积。因此，文章提出了一种基于熵模型的激光雷达点云帧间编码方法。为应对激光雷达点云序列的时间冗余问题，利用参考点云与待编码点云的位姿信息，提出一种有效消除点云序列中时域冗余的帧间编码方法。为去除点云的空间冗余问题，将原始点云数据转换成适合小波变换的密集二维矩阵数据，通过小波变换能够有效地利用二维矩阵的空间相关性。通过CDF5/3小波变换对二维矩阵进行小波变换得到小波系数，通过对熵模型训练后的熵参数进行算术编码从而得到更加紧凑的比特流。实验结果表明，提出的设计方法与G-PCC、PCL编码方法相比具有较高的编码性能。
Keyword ：
位姿信息获取 位姿信息获取 小波变换 小波变换 点云压缩 点云压缩 熵模型 熵模型
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 石元龙 , 郑明魁 , 丁志洋 et al. 基于熵模型的激光雷达点云帧间编码方法 [J]. | 信息技术与信息化 , 2025 , PageCount-页数: 4 (04) : 176-179 . |
| MLA | 石元龙 et al. "基于熵模型的激光雷达点云帧间编码方法" . | 信息技术与信息化 PageCount-页数: 4 . 04 (2025) : 176-179 . |
| APA | 石元龙 , 郑明魁 , 丁志洋 , 宋广胜 . 基于熵模型的激光雷达点云帧间编码方法 . | 信息技术与信息化 , 2025 , PageCount-页数: 4 (04) , 176-179 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
针对当前视频预测算法在生成视频帧时细节模糊、精度较低的问题,提出了一种基于边缘增强和多尺度时空重组的视频预测方法.首先通过频域分离技术,将视频帧划分为高频信息和低频信息,并对二者分别进行针对性处理.其次,设计了高频边缘增强模块,专注于高频边缘特征的学习与优化.同时,引入多尺度时空重组模块,针对低频结构信息,深入挖掘其时空依赖性.最终将高低频特征进行充分融合,用以生成高质量的预测视频帧.实验结果表明,与现有先进算法相比,该方法在预测性能上实现了提升,充分验证了其有效性.
Keyword ：
多尺度时空重组 多尺度时空重组 视频预测 视频预测 边缘增强 边缘增强 频域分离 频域分离
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 吴孔贤 , 郑明魁 . 基于边缘增强和多尺度时空重组的视频预测方法 [J]. | 网络安全与数据治理 , 2025 , 44 (3) : 22-26 . |
| MLA | 吴孔贤 et al. "基于边缘增强和多尺度时空重组的视频预测方法" . | 网络安全与数据治理 44 . 3 (2025) : 22-26 . |
| APA | 吴孔贤 , 郑明魁 . 基于边缘增强和多尺度时空重组的视频预测方法 . | 网络安全与数据治理 , 2025 , 44 (3) , 22-26 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
针对传统的激光SLAM算法在室外动态场景下定位精度低和缺少语义信息等问题,本文设计了一种基于语义信息融合的激光SLAM改进算法,并在公开数据集KITTI上进行测试实验,为提升整体位姿估计精度和建图精度提供有益参考.
Keyword ：
LeGO-LOAM LeGO-LOAM 深度学习 深度学习 激光SLAM 激光SLAM 语义分割 语义分割 语义约束 语义约束
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 王占宝 , 郑明魁 . 融合语义信息的激光SLAM研究 [J]. | 广播电视网络 , 2024 , 31 (5) : 28-30 . |
| MLA | 王占宝 et al. "融合语义信息的激光SLAM研究" . | 广播电视网络 31 . 5 (2024) : 28-30 . |
| APA | 王占宝 , 郑明魁 . 融合语义信息的激光SLAM研究 . | 广播电视网络 , 2024 , 31 (5) , 28-30 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Sampling is a crucial concern for outdoor light detection and ranging (LiDAR) point cloud registration due to the large amounts of point cloud. Numerous algorithms have been devised to tackle this issue by selecting key points. However, these approaches often necessitate extensive computations, giving rise to challenges related to computational time and complexity. This letter proposes a multi-domain uniform sampling method (MDU-sampling) for large-scale outdoor LiDAR point cloud registration. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains. First, uniform sampling is executed in the spatial domain, maintaining local point cloud uniformity. This is believed to preserve more potential point correspondences and is beneficial for subsequent neighbourhood information aggregation and feature sampling. Subsequently, a secondary sampling in the feature domain is performed to reduce redundancy among the features of neighbouring points. Notably, only points on the same ring in LiDAR data are considered as neighbouring points, eliminating the need for additional neighbouring point search and thereby speeding up processing rates. Experimental results demonstrate that the approach enhances accuracy and robustness compared with benchmarks. The feature extraction based on deep learning aggregates information from the neighbourhood, so there is redundancy between adjacent features. The sampling method in this paper is carried out in the spatial and feature domains, reducing the computational resources for registration. The proposed method preserves more effective information compared to other algorithms. Points are only considered on the same ring in LiDAR data as neighbouring points, eliminating the need for additional neighbouring point search. This makes it efficient and suitable for large-scale outdoor LiDAR point cloud registration. image
Keyword ：
artificial intelligence artificial intelligence robot vision robot vision signal processing signal processing SLAM (robots) SLAM (robots)
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ou, Wengjun , Zheng, Mingkui , Zheng, Haifeng . MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration [J]. | ELECTRONICS LETTERS , 2024 , 60 (5) . |
| MLA | Ou, Wengjun et al. "MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration" . | ELECTRONICS LETTERS 60 . 5 (2024) . |
| APA | Ou, Wengjun , Zheng, Mingkui , Zheng, Haifeng . MDU-sampling: Multi-domain uniform sampling method for large-scale outdoor LiDAR point cloud registration . | ELECTRONICS LETTERS , 2024 , 60 (5) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The comprehension of 3D semantic scenes holds paramount significance in autonomous driving and robotics technology. Nevertheless, the simultaneous achievement of real-time processing and high precision in complex, expansive outdoor environments poses a formidable challenge. In response to this challenge, we propose a novel occupancy network named RTONet, which is built on a teacher-student model. To enhance the ability of the network to recognize various objects, the decoder incorporates dilated convolution layers with different receptive fields and utilizes a multi-path structure. Furthermore, we develop an automatic frame selection algorithm to augment the guidance capability of the teacher network. The proposed method outperforms the existing grid-based approaches in semantic completion (mIoU), and achieves the state-of-the-art performance in terms of real-time inference speed while exhibiting competitive performance in scene completion (IoU) on the SemanticKITTI benchmark.
Keyword ：
Decoding Decoding deep learning for visual perception deep learning for visual perception Feature extraction Feature extraction Laser radar Laser radar LiDAR LiDAR mapping mapping occupancy grid occupancy grid Point cloud compression Point cloud compression Real-time systems Real-time systems Semantics Semantics Semantic scene understanding Semantic scene understanding Three-dimensional displays Three-dimensional displays
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lai, Quan , Zheng, Haifeng , Feng, Xinxin et al. RTONet: Real-Time Occupancy Network for Semantic Scene Completion [J]. | IEEE ROBOTICS AND AUTOMATION LETTERS , 2024 , 9 (10) : 8370-8377 . |
| MLA | Lai, Quan et al. "RTONet: Real-Time Occupancy Network for Semantic Scene Completion" . | IEEE ROBOTICS AND AUTOMATION LETTERS 9 . 10 (2024) : 8370-8377 . |
| APA | Lai, Quan , Zheng, Haifeng , Feng, Xinxin , Zheng, Mingkui , Chen, Huacong , Chen, Wenqiang . RTONet: Real-Time Occupancy Network for Semantic Scene Completion . | IEEE ROBOTICS AND AUTOMATION LETTERS , 2024 , 9 (10) , 8370-8377 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
无线电频谱监测海量数据存储和分析是无线电监管工作的重要组成部分.频谱数据具有时间相关性以及不同频点间的相关冗余,对此本文设计了一种基于类小波变换的无线电频谱监测数据无损压缩方法.该方法首先基于时间相关性将一维频谱信号转换成二维矩阵;转换成二维矩阵后数据在水平方向以及垂直方向都存在冗余,算法采用卷积神经网络来代替传统小波中的预测和更新模块,并引入了自适应压缩块来处理不同维度的特征,从而获得更紧凑的频谱数据表示.研究进一步设计了一种基于上下文的深度熵模型,利用类小波变换不同子带系数获得熵编码参数,以此估计累积概率,从而实现频谱数据的压缩.实验结果表明,与已有的Deflate等传统频谱监测数据无损压缩方法相比,本文算法有进一步的性能提升,与典型的JPEG2000、PNG、JPEG-LS等二维图像无损压缩方法相比,本文所提出的方法的压缩效果也提高了20％以上.
Keyword ：
卷积神经网络 卷积神经网络 无损压缩 无损压缩 熵编码 熵编码 类小波变换 类小波变换 频谱监测数据 频谱监测数据
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 张承琰 , 郑明魁 , 刘会明 et al. 一种基于类小波变换的无线电频谱监测数据无损压缩方法 [J]. | 电子测量与仪器学报 , 2024 , 38 (7) : 152-158 . |
| MLA | 张承琰 et al. "一种基于类小波变换的无线电频谱监测数据无损压缩方法" . | 电子测量与仪器学报 38 . 7 (2024) : 152-158 . |
| APA | 张承琰 , 郑明魁 , 刘会明 , 易天儒 , 李少良 , 陈祖儿 . 一种基于类小波变换的无线电频谱监测数据无损压缩方法 . | 电子测量与仪器学报 , 2024 , 38 (7) , 152-158 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
For moving cameras, the video content changes significantly, which leads to inaccurate prediction in traditional inter prediction and results in limited compression efficiency. To solve these problems, first, we propose a camera pose-based background modeling (CP-BM) framework that uses the camera motion and the textures of reconstructed frames to model the background of the current frame. Compared with the reconstructed frames, the predicted background frame generated by CP-BM is more geometrically similar to the current frame in position and is more strongly correlated with it at the pixel level; thus, it can serve as a higher-quality reference for inter prediction, and the compression efficiency can be improved. Second, to compensate the motion of the background pixels, we construct a pixel-level motion vector field that can accurately describe various complex motions with only a small overhead. Our method is more general than other motion models because it has more degrees of freedom, and when the degrees of freedom are decreased, it encompasses other motion models as special cases. Third, we propose an optical flow-based depth estimation (OF-DE) method to synchronize the depth information at the codec, which is used to build the motion vector field. Finally, we integrate the overall scheme into the High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) reference software HM-16.7 and VTM-10.0. Experimental results demonstrate that in HM-16.7, for in-vehicle video sequences, our solution has an average Bj & oslash;ntegaard delta bit rate (BD-rate) gain of 8.02% and reduces the encoding time by 20.9% due to the superiority of our scheme in motion estimation. Moreover, in VTM-10.0 with affine motion compensation (MC) turned off and turned on, our method has average BD-rate gains of 5.68% and 0.56%, respectively.
Keyword ：
background modeling background modeling Bit rate Bit rate camera pose camera pose Cameras Cameras Computational modeling Computational modeling Encoding Encoding Estimation Estimation moving cameras moving cameras Predictive models Predictive models Video coding Video coding
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Fang, Zheng , Zheng, Mingkui , Chen, Pingping et al. Camera Pose-Based Background Modeling for Video Coding in Moving Cameras [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (5) : 4054-4069 . |
| MLA | Fang, Zheng et al. "Camera Pose-Based Background Modeling for Video Coding in Moving Cameras" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 5 (2024) : 4054-4069 . |
| APA | Fang, Zheng , Zheng, Mingkui , Chen, Pingping , Chen, Zhifeng , Oliver Wu, Dapeng . Camera Pose-Based Background Modeling for Video Coding in Moving Cameras . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (5) , 4054-4069 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
本发明提出一种基于激光雷达的点云实时采集压缩传输系统及方法，包括：实时采集激光雷达点云，对点云进行自适应编码和封装，实时传输，解封装和自适应解码，渲染可视化并保存本地。本系统具有时间复杂度低，实时性高的优点，根据带宽动态压缩后的数据在低带宽的情况下也可实现可靠低时延的传输，远程实时地观测并处理激光雷达采集的第一手3D点云数据。高带宽情况下该系统还可用于传输多路数据，符合车路协同、远程智能驾驶、机器人视觉等行业对远程采集传输点云数据并进行分析处理的低时延需求。
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 陈建 , 黄炜 , 陈锋 et al. 基于激光雷达的点云实时采集压缩传输系统及方法 : CN202111074168.3[P]. | 2021-09-14 00:00:00 . |
| MLA | 陈建 et al. "基于激光雷达的点云实时采集压缩传输系统及方法" : CN202111074168.3. | 2021-09-14 00:00:00 . |
| APA | 陈建 , 黄炜 , 陈锋 , 郑明魁 , 黄昕 . 基于激光雷达的点云实时采集压缩传输系统及方法 : CN202111074168.3. | 2021-09-14 00:00:00 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Video based point cloud compression (V-PCC) provides an efficient solution for compressing dynamic point clouds, but the projection of V-PCC from 3D to 2D destroys the correlation of 3D inter-frame motion and reduces the performance of inter-frame coding. To solve this problem, we proposes an adaptive segmentation based multi-mode inter-frame coding method for video point cloud to improve V-PCC, and designs a new dynamic point cloud inter-frame encoding framework. Firstly, in order to achieve more accurate block prediction, a block matching method based on adaptive regional segmentation is proposed to find the best matching block; Secondly, in order to further improve the performance of inter coding, a multi-mode inter-frame coding method based on joint attribute rate distortion optimization (RDO) is proposed to increase the prediction accuracy and reduce the bit rate consumption. Experimental results show that the improved algorithm proposed in this paper achieves -22.57% Bjontegaard delta bit rate (BD-BR) gain compared with V-PCC. The algorithm is especially suitable for dynamic point cloud scenes with little change between frames, such as video surveillance and video conference. © 2023 Science Press. All rights reserved.
Keyword ：
Electric distortion Electric distortion Image coding Image coding Image compression Image compression Security systems Security systems Signal distortion Signal distortion Video signal processing Video signal processing
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Jian , Liao, Yan-Jun , Wang, Kuo et al. An Adaptive Segmentation Based Multi-mode Inter-frame Coding Method for Video Point Cloud [J]. | Acta Automatica Sinica , 2023 , 49 (8) : 1707-1722 . |
| MLA | Chen, Jian et al. "An Adaptive Segmentation Based Multi-mode Inter-frame Coding Method for Video Point Cloud" . | Acta Automatica Sinica 49 . 8 (2023) : 1707-1722 . |
| APA | Chen, Jian , Liao, Yan-Jun , Wang, Kuo , Zheng, Ming-Kui , Su, Li-Chao . An Adaptive Segmentation Based Multi-mode Inter-frame Coding Method for Video Point Cloud . | Acta Automatica Sinica , 2023 , 49 (8) , 1707-1722 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Depth estimation from a single image is a fundamental problem in the field of computer vision. With the great success of deep learning techniques, various self-supervised monocular depth estimation methods using encoder-decoder architectures have emerged. However, most previous approaches regress the depth map directly using a single encoder-decoder structure, which may not obtain sufficient features in the image and results in a depth map with low accuracy and blurred details. To improve the accuracy of self-supervised monocular depth estimation, we propose a simple but very effective scheme for depth estimation using a dual encoder-decoder structure network. Specifically, we introduce a novel global feature extraction network (GFN) to extract global features from images. GFN includes PoolAttentionFormer and ResBlock, which work together to extract and fuse hierarchical global features into the depth estimation network (DEN). To further improve the accuracy, we design two feature fusion mechanisms, including global feature fusion and multiscale fusion. The experimental results of various dual encoder-decoder combination schemes tested on the KITTI dataset show that our proposed one is effective in improving the accuracy of self-supervised monocular depth estimation, which reached 89.6% (delta < 1.25).
Keyword ：
Accuracy Accuracy Convolutional neural networks Convolutional neural networks Data mining Data mining Decoding Decoding dual encoder-decoder dual encoder-decoder Estimation Estimation Feature extraction Feature extraction Fuses Fuses global information global information monocular depth estimation monocular depth estimation self-supervised self-supervised Training Training
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zheng, Mingkui , Luo, Lin , Zheng, Haifeng et al. A Dual Encoder-Decoder Network for Self-Supervised Monocular Depth Estimation [J]. | IEEE SENSORS JOURNAL , 2023 , 23 (17) : 19747-19756 . |
| MLA | Zheng, Mingkui et al. "A Dual Encoder-Decoder Network for Self-Supervised Monocular Depth Estimation" . | IEEE SENSORS JOURNAL 23 . 17 (2023) : 19747-19756 . |
| APA | Zheng, Mingkui , Luo, Lin , Zheng, Haifeng , Ye, Zhangfan , Su, Zhe . A Dual Encoder-Decoder Network for Self-Supervised Monocular Depth Estimation . | IEEE SENSORS JOURNAL , 2023 , 23 (17) , 19747-19756 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
