Li Guan Marc Pollefeys, 2008
In this paper, we propose a unified calibration technique for a heterogeneous sensor network of video camcorders and Time-of-Flight (ToF) cameras. By moving a spherical calibration target around the commonly observed scene, we can robustly and conveniently extract the sphere centers in the observed images and recover the geometric extrinsics for both types of sensors. The approach is then evaluated with a real dataset of two HD camcorders and two ToF cameras, and 3D shapes are reconstructed from this calibrated system. The main contributions are: (1) We reveal the fact that the frontmost sphere surface point to the ToF camera center is always highlighted, and use this idea to extract sphere centers in the ToF camera images; (2) We propose a unified calibration scheme in spite of the heterogeneity of the sensors. After the calibration, this multi-modal sensor network thus becomes powerful to generate high-quality 3D shapes efficiently.
Huan Du, Thierry Oggier, Felix Lustenberger, Edoardo Charbon, 2005
In this paper, a complete system is presented which mimics a QWERTY keyboard on an arbitrary surface. The system consists of a pattern projector and a true-3D range camera for detecting the typing events. We exploit depth information acquired with the 3D range camera and detect the hand region using a pre-computed reference frame. The fingertips are found by analyzing the hands’ contour and fitting the depth curve with different feature models. To detect a keystroke, we analyze the feature of the depth curve and map it back to a global coordinate system to find which key was pressed. These steps are fully automated and do not require human intervention. The system can be used in any application requiring zero form factor and minimized or no contact with a medium, as in a large number of cases in human-to-computer interaction, virtual reality, game control, 3D designs, etc.
Li Guan, Jean-Sebastien Franco, Marc Pollefeys, 2008
In this paper, we reconstruct 3D objects with a heterogeneous sensor network of Time of Flight (ToF) Range Imaging (RIM) sensors and high-res camcorders. With this setup, we first carry out a simple but effective depth calibration for the RIM cameras. We then combine the camcorder silhouette cues and RIM camera depth information, for the reconstruction. Our main contribution is the proposal of a sensor fusion framework so that the computation is general, simple and scalable. Although we only discuss the fusion of conventional cameras and RIM cameras in this paper, the proposed framework can be applied to any vision sensors. This framework uses a space occupancy grid as a probabilistic 3D representation of scene contents. After defining sensing models for each type of sensors, the reconstruction simply is a Bayesian inference problem, and can be solved robustly. The experiments show that the quality of the reconstruction is substantially improved from the noisy depth sensor measurement.
Stefan Fuchs and Stefan May
This paper presents a method for precise surface reconstruction with time-of-flight (TOF) cameras. A novel calibration approach which simplifies the calibration task and doubles the camera’s precision is developed and compared to current calibration methods. Remaining errors are tackled by applying filter and error distributing methods. Thus, a reference object is circumferentially reconstructed with an overall mean precision of approximately 3mm in translation and 3 deg in rotation. The resulting model serves as quantification of achievable
reconstruction precision with TOF cameras. This is a major criteria for the potential analysis of this sensor technology, that is firstly demonstrated within this work.
Cang Ye and GuruPrasad M. Hegde
This paper presents a new method for extracting object edges from range images obtained by a 3D range imaging sensor⎯the SwissRanger SR-3000. In range image preprocessing tage, the method enhances object edges by using surface normal information; and it employs the Hough Transform to detect straight line features in the Normal-Enhanced Range Image NERI). Due to the noise in the sensor’s range data, a NERI contains corrupted object surfaces that may result in unwanted edges and greatly encumber the extraction of linear features. To alleviate this problem, a Singular Value Decomposition (SVD) filter is developed to smooth object surfaces. The efficacy of the edge extraction method is validated by experiments in various environments.