Generation of a depth-map from 2D video is the kernel of DIBR (Depth Image Based Rendering) in 2D-3D video conversion systems. However it occupies over most of the system resource where the motion search module takes ...Generation of a depth-map from 2D video is the kernel of DIBR (Depth Image Based Rendering) in 2D-3D video conversion systems. However it occupies over most of the system resource where the motion search module takes up 90% time-consuming in typical motion estimation-based depth-map generation algorithms. In order to reduce the computational complexity, in this paper a new fast depth-map generation algorithm based on motion search is developed, in which a fast diamond search algorithm is adopted to decide whether a 16x16 or 4x4 block size is used based on Sobel operator in the motion search module to obtain a sub-depth-map. Then the sub-depth-map will be fused with the sub-depth-maps gotten from depth from color component Cr and depth from linear perspective modules to compensate and refine detail of the depth-map, finally obtain a better depth-map. The simulation results demonstrate that the new approach can greatly reduce over 50% computational complexity compared to other existing methods.展开更多
The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) g...The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.展开更多
根据公共信息模型(common information model,CIM)的特点,提出基于CIM的配电网数据映射到关系数据库的一些规则,并基于建立的数据库提出一种改进的深度优先搜索算法以完成对配电网最短路径的搜索,最后以IEEE69节点系统为算例进行验证,...根据公共信息模型(common information model,CIM)的特点,提出基于CIM的配电网数据映射到关系数据库的一些规则,并基于建立的数据库提出一种改进的深度优先搜索算法以完成对配电网最短路径的搜索,最后以IEEE69节点系统为算例进行验证,结果表明该算法不仅搜索速度快、节省内存,而且能够满足配电网重构和可靠性分析对拓扑结构的要求。展开更多
针对三维高效视频编码(3D High Efficiency Video Coding,3D-HEVC)增加的深度数据引入极高复杂度和资源消耗的问题,利用3D-HEVC的软件测试模型HTM16.1,对帧内预测算法的深度图进行分析,充分利用深度图中楔形分割具有相邻边缘分割和相对...针对三维高效视频编码(3D High Efficiency Video Coding,3D-HEVC)增加的深度数据引入极高复杂度和资源消耗的问题,利用3D-HEVC的软件测试模型HTM16.1,对帧内预测算法的深度图进行分析,充分利用深度图中楔形分割具有相邻边缘分割和相对边缘分割的特点,提出了一种精简楔形搜索模板.实验表明,所提出的优化方案在不改变视频编码质量的情况下,节约了99.2%的存储空间,减少了61.8%的编码时间.此外,针对楔形波在视频测试平台上串行执行时间较长、存储消耗较大等缺点,考虑到提出的精简楔形搜索模板间无数据相关性,充分利用项目组提供的阵列处理器(DPR-CODEC)天然并行的特性,提出了一种帧内预测模式并行方案.所设计的并行方案数据加载时间的串/并加速比为1.912,在执行编码时各模板的串/并加速比达到1.637.展开更多
文摘Generation of a depth-map from 2D video is the kernel of DIBR (Depth Image Based Rendering) in 2D-3D video conversion systems. However it occupies over most of the system resource where the motion search module takes up 90% time-consuming in typical motion estimation-based depth-map generation algorithms. In order to reduce the computational complexity, in this paper a new fast depth-map generation algorithm based on motion search is developed, in which a fast diamond search algorithm is adopted to decide whether a 16x16 or 4x4 block size is used based on Sobel operator in the motion search module to obtain a sub-depth-map. Then the sub-depth-map will be fused with the sub-depth-maps gotten from depth from color component Cr and depth from linear perspective modules to compensate and refine detail of the depth-map, finally obtain a better depth-map. The simulation results demonstrate that the new approach can greatly reduce over 50% computational complexity compared to other existing methods.
基金the National Natural Science Foundation of China (61720106012 and 61403215)the Foundation of State Key Laboratory of Robotics (2006-003)the Fundamental Research Funds for the Central Universities for the financial support of this work.
文摘The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.
文摘根据公共信息模型(common information model,CIM)的特点,提出基于CIM的配电网数据映射到关系数据库的一些规则,并基于建立的数据库提出一种改进的深度优先搜索算法以完成对配电网最短路径的搜索,最后以IEEE69节点系统为算例进行验证,结果表明该算法不仅搜索速度快、节省内存,而且能够满足配电网重构和可靠性分析对拓扑结构的要求。
文摘针对三维高效视频编码(3D High Efficiency Video Coding,3D-HEVC)增加的深度数据引入极高复杂度和资源消耗的问题,利用3D-HEVC的软件测试模型HTM16.1,对帧内预测算法的深度图进行分析,充分利用深度图中楔形分割具有相邻边缘分割和相对边缘分割的特点,提出了一种精简楔形搜索模板.实验表明,所提出的优化方案在不改变视频编码质量的情况下,节约了99.2%的存储空间,减少了61.8%的编码时间.此外,针对楔形波在视频测试平台上串行执行时间较长、存储消耗较大等缺点,考虑到提出的精简楔形搜索模板间无数据相关性,充分利用项目组提供的阵列处理器(DPR-CODEC)天然并行的特性,提出了一种帧内预测模式并行方案.所设计的并行方案数据加载时间的串/并加速比为1.912,在执行编码时各模板的串/并加速比达到1.637.
