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The ability of drones to access disaster areas has been proven powerful and flexible for acquiring first-hand optical imagery data for environmental observation. However, such imagery data usually undergo postprocessing, and the three-dimensional (3D) products are mainly for accurate land surveys. The postprocessing procedure is too time-consuming to meet instant decision support and rescue response requirements. Therefore, this paper intends to develop a systematic workflow that is able to achieve on-the-fly 3D reconstruction in disaster areas by optical imagery sequentially acquired by drones. This study proposes a strategy to spatially link sequential images (SLSI) for image localization and suitable stereopair selection. In addition, the criteria for valid epipolar stereoapair determination are developed to make the 3D dense reconstruction more automatic and effective. The 3D digital land surface can be gradually reconstructed and expanded in the computer system while the drone is capturing new images. This paper utilizes the imagery dataset of collapsed buildings induced by the 2016 Kumamoto earthquake in Japan to simulate the more effective 3D reconstruction. Although the accuracy of the consequence is reported to be closely one meter, the mean data processing time for every image can achieve the level by approximately ten seconds while performing the proposed scheme on an iMac with Intel Core i5 and 16 GB random access memory (RAM). As a result, the efficiency and computational power needed are significantly reduced to support emergency applications soon after a disaster occurs. • Efficient and automatic image selection and localization • Gradual on-the-fly 3D land surface reconstruction • Addressing 3D reconstruction without direct-georeferencing data • Reduction of image process complexity and cost [ABSTRACT FROM AUTHOR]

We developed a 3D archive system for Japanese traditional performing arts. The system generates sequences of 3D actor models of the performances from multi-view video by using a graph-cuts algorithm and stores them with CG background models and related information. The system can show a scene from any viewpoint as follows; the 3D actor model is integrated with the background model and the integrated model is projected to a viewpoint that the user indicates with a viewpoint controller. A challenge of generating the actor models is how to reconstruct thin or slender parts. Japanese traditional costumes for performances include slender parts such as long sleeves, fans and strings that may be manipulated during the performance. The graph-cuts algorithm is a powerful 3D reconstruction tool but it tends to cut off those parts because it uses an energy-minimization process. Hence, the search for a way to reconstruct such parts is important for us to preserve these arts for future generations. We therefore devised an adaptive erosion method that works on the visual hull and applied it to the graph-cuts algorithm to extract interior nodes in the thin parts and to prevent the thin parts from being cut off. Another tendency of the reconstruction method using the graph-cuts algorithm is over-shrinkage of the reconstructed models. This arises because the energy can also be reduced by cutting inside the true surface. To avoid this tendency, we applied a silhouette-rim constraint defined by the number of the silhouette-rims passing through each node. By applying the adaptive erosion process and the silhouette-rim constraint, we succeeded in constructing a virtual performance with costumes including thin parts. This paper presents the results of the 3D reconstruction using the proposed method and some outputs of the 3D archive system. [ABSTRACT FROM AUTHOR]

Camera parameter recovery from an image sequence is very important in many applications such as 3D model reconstruction, object recognition, robot navigation, and mixed reality. However, there is a problem concerning the precision in camera parameter estimation from the image sequence, because feature tracking errors and estimation errors are accumulated. In this paper, we propose a camera parameter estimation method which is based on using a number of markers with known 3D position, color, and shape, as well as natural features. Initially, the camera parameters and 3D positions of natural features are estimated efficiently in every frame by tracking both these markers and natural features. The accumulation of estimated errors is then minimized by specifying additional markers in some frames of input and optimizing parameters globally through the whole input. In the experiments, some results of 3D reconstruction show the validity of the proposed method. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(8): 12–20, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10702 [ABSTRACT FROM AUTHOR]