Real-Time, Three-Dimensional Object Detection and Modeling in Construction

Jochen Teizer, Frederic Bosche, Carlos H. Caldas, Carl T. Haas, and Katherine A. Liapi, 2005

pdficon_largeThis paper describes a research effort directed to produce methods to model three-dimensional scenes of construction field objects in real-time that adds valuable data to construction information management systems, as well as equipment navigation systems. For efficiency reasons, typical construction objects are modeled by bounding surfaces using a high-frame rate range sensor, called Flash LADAR. The sensor provides a dense cloud of range points which are segmented and grouped into objects. Algorithms are being developed to accurately detect these objects and model characteristics such as volume, speed, and direction. Initial experiments show the feasibility of this method. The advantages and limitations, and potential solutions to limitations are summarized in this paper.

3D Object Reconstruction with Heterogeneous Sensor Data

Li Guan, Jean-Sebastien Franco, Marc Pollefeys, 2008

pdficon_largeIn 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.