Search

3D human trajectory reconstruction in an indoor scene is critical in various applications, such as indoor navigation and human activity recognition. This task is challenging due to occlusion and clutters of indoor scenes, flexible human body joints, and severe lack of relevant datasets. Although several methods have been proposed to reconstruct a 3D human trajectory, they either can recover only 2D positions or require human initiative cooperation. In this paper, we propose a novel framework for 3D human trajectory reconstruction in an indoor scene using monocular surveillance videos and static point clouds without any initiative cooperation. The proposed framework consists of three modules: 3D pose estimation, depth regression, and trajectory reconstruction. We first estimate 3D pose from videos. Especially, we reconstruct a half-body 3D pose to deal with the occlusion problem. Then, we propose a depth regression approach to iteratively regress the depth of a 3D pose. Unlike data-driven approaches, our depth regression approach does not require training data and can be integrated into any 3D pose model. Finally, we exploit the geometric constraints from the point cloud to optimize the 3D trajectory. We evaluated the 3D pose estimation and depth regression modules on the H3.6M datasets. Due to the lack of evaluation datasets, we also built a trajectory dataset to evaluate the trajectory reconstruction performance. Empirical evaluation shows that our framework achieves accurate trajectory reconstruction results on real-world videos. [ABSTRACT FROM AUTHOR]

Efficient 3-D mapping provides useful and detailed 3-D data for many applications. In this letter, we present a multisensor calibration and mapping method, to provide highly efficient and relatively accurate colored mapping for GPS-/global navigation satellite system-denied environments. The sensor data include 3-D laser scanning point clouds and camera images. A simultaneous localization and mapping (SLAM)-assisted calibration method is first proposed for multiple multibeam light detection and ranging (LiDAR) and multiple camera calibration. An improved SLAM method with loop closure is proposed for 3-D mapping. With the proposed calibration and mapping methods, centimeter-level colored point clouds can be obtained efficiently. The proposed method was tested with both backpacked and car-mounted systems on indoor and outdoor scenes. Experimental results show the effectiveness and efficiency of the proposed calibration and mapping methods. [ABSTRACT FROM AUTHOR]

This letter presents a line structure-based method for integration of centimeter-level indoor backpacked scanning point clouds and millimeter-level outdoor terrestrial laser scanning point clouds. Using 3-D lines for registration, instead of matching points directly, can improve the robustness of the method and adapt to multisource point cloud data of different qualities. Considering the limited overlapping between indoor and outdoor scenes, line structures are extracted from overlapped wall areas that may be included in interior and exterior data. Here, a patch-based method labels a point cloud into wall, ceiling, floor categories, as well as assigning the candidate overlapping walls. Then, lines structures are extracted from the wall plane point cloud. Potential door and window line structures are detected and refined for point cloud registration. Last, an iterative closest point-based method is used to fine tune the registration results. Our results show that the proposed method effectively integrates a promising map of indoor and outdoor scenes. [ABSTRACT FROM AUTHOR]