Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
The Ball-Pivoting Algorithm for Surface Reconstruction
IEEE Transactions on Visualization and Computer Graphics
The Visual Hull Concept for Silhouette-Based Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
International Journal of Robotics Research
Next-generation 3D visualization for visual surveillance
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
Real-time 4d reconstruction of human motion
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
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This paper reports on a pilot system for reconstruction and visualisation of complex spatio-temporal scenes by integrating two different types of data: outdoor 4D data measured by a rotating multi-beam LIDAR sensor, and 4D models of moving actors obtained in a 4D studio. A typical scenario is an outdoor scene with multiple walking pedestrians. The LIDAR monitors the scene from a fixed position and provides a dynamic point cloud. This information is processed to build a 3D model of the environment and detect and track the pedestrians. Each of them is represented by a point cluster and a trajectory. A moving cluster is then substituted by a detailed 4D model created in the studio. The output is a geometrically reconstructed and textured scene with avatars that follow in real time the trajectories of the pedestrians.