In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requi...In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.展开更多
Cotton is one of the most important economic crops in the world,and it provides natural fiber for the textile industry.With the advancement of the textile technology and increased consumption demands on cotton fiber,b...Cotton is one of the most important economic crops in the world,and it provides natural fiber for the textile industry.With the advancement of the textile technology and increased consumption demands on cotton fiber,both cotton yield and quality should be enhanced.However,cotton yield展开更多
The point cloud data obtained by airborne radar equipment has high-precision three-dimensional spatial data information, which provides a new technical means for making high-precision DEM. Although the production of l...The point cloud data obtained by airborne radar equipment has high-precision three-dimensional spatial data information, which provides a new technical means for making high-precision DEM. Although the production of lidar system hardware equipment is relatively mature, the existing data filtering and data interpolation algorithms have their own advantages and disadvantages, and it is difficult for any algorithm to meet the needs of various terrain classification. Therefore, the exploration of airborne radar technology point cloud data processing and application, and the development of more intelligent, accurate, and highly adaptable point cloud filtering algorithms are worthy of our attention and research, which are of great significance to improving the quality of DEM generation and the development and application of future airborne radar technology.展开更多
Efficient perception of the real world is a long-standing effort of computer vision.Mod⁃ern visual computing techniques have succeeded in attaching semantic labels to thousands of daily objects and reconstructing dens...Efficient perception of the real world is a long-standing effort of computer vision.Mod⁃ern visual computing techniques have succeeded in attaching semantic labels to thousands of daily objects and reconstructing dense depth maps of complex scenes.However,simultaneous se⁃mantic and spatial joint perception,so-called dense 3D semantic mapping,estimating the 3D ge⁃ometry of a scene and attaching semantic labels to the geometry,remains a challenging problem that,if solved,would make structured vision understanding and editing more widely accessible.Concurrently,progress in computer vision and machine learning has motivated us to pursue the capability of understanding and digitally reconstructing the surrounding world.Neural metric-se⁃mantic understanding is a new and rapidly emerging field that combines differentiable machine learning techniques with physical knowledge from computer vision,e.g.,the integration of visualinertial simultaneous localization and mapping(SLAM),mesh reconstruction,and semantic un⁃derstanding.In this paper,we attempt to summarize the recent trends and applications of neural metric-semantic understanding.Starting with an overview of the underlying computer vision and machine learning concepts,we discuss critical aspects of such perception approaches.Specifical⁃ly,our emphasis is on fully leveraging the joint semantic and 3D information.Later on,many im⁃portant applications of the perception capability such as novel view synthesis and semantic aug⁃mented reality(AR)contents manipulation are also presented.Finally,we conclude with a dis⁃cussion of the technical implications of the technology under a 5G edge computing scenario.展开更多
基金supported by the National Natural Science Foundation of China(61473202)。
文摘In recent years, reconstructing a sparse map from a simultaneous localization and mapping(SLAM) system on a conventional CPU has undergone remarkable progress. However,obtaining a dense map from the system often requires a highperformance GPU to accelerate computation. This paper proposes a dense mapping approach which can remove outliers and obtain a clean 3D model using a CPU in real-time. The dense mapping approach processes keyframes and establishes data association by using multi-threading technology. The outliers are removed by changing detections of associated vertices between keyframes. The implicit surface data of inliers is represented by a truncated signed distance function and fused with an adaptive weight. A global hash table and a local hash table are used to store and retrieve surface data for data-reuse. Experiment results show that the proposed approach can precisely remove the outliers in scene and obtain a dense 3D map with a better visual effect in real-time.
文摘Cotton is one of the most important economic crops in the world,and it provides natural fiber for the textile industry.With the advancement of the textile technology and increased consumption demands on cotton fiber,both cotton yield and quality should be enhanced.However,cotton yield
文摘The point cloud data obtained by airborne radar equipment has high-precision three-dimensional spatial data information, which provides a new technical means for making high-precision DEM. Although the production of lidar system hardware equipment is relatively mature, the existing data filtering and data interpolation algorithms have their own advantages and disadvantages, and it is difficult for any algorithm to meet the needs of various terrain classification. Therefore, the exploration of airborne radar technology point cloud data processing and application, and the development of more intelligent, accurate, and highly adaptable point cloud filtering algorithms are worthy of our attention and research, which are of great significance to improving the quality of DEM generation and the development and application of future airborne radar technology.
文摘Efficient perception of the real world is a long-standing effort of computer vision.Mod⁃ern visual computing techniques have succeeded in attaching semantic labels to thousands of daily objects and reconstructing dense depth maps of complex scenes.However,simultaneous se⁃mantic and spatial joint perception,so-called dense 3D semantic mapping,estimating the 3D ge⁃ometry of a scene and attaching semantic labels to the geometry,remains a challenging problem that,if solved,would make structured vision understanding and editing more widely accessible.Concurrently,progress in computer vision and machine learning has motivated us to pursue the capability of understanding and digitally reconstructing the surrounding world.Neural metric-se⁃mantic understanding is a new and rapidly emerging field that combines differentiable machine learning techniques with physical knowledge from computer vision,e.g.,the integration of visualinertial simultaneous localization and mapping(SLAM),mesh reconstruction,and semantic un⁃derstanding.In this paper,we attempt to summarize the recent trends and applications of neural metric-semantic understanding.Starting with an overview of the underlying computer vision and machine learning concepts,we discuss critical aspects of such perception approaches.Specifical⁃ly,our emphasis is on fully leveraging the joint semantic and 3D information.Later on,many im⁃portant applications of the perception capability such as novel view synthesis and semantic aug⁃mented reality(AR)contents manipulation are also presented.Finally,we conclude with a dis⁃cussion of the technical implications of the technology under a 5G edge computing scenario.
