A computational framework for performance characterization of three-dimensional reconstruction techniques from sequence of images

This dissertation addresses the problem of performance characterization of 3-D reconstruction techniques from a sequence of images. Although many 3-D reconstruction techniques have been proposed in other literature, the work done to quantify their performance is quite insufficient from the computational point-of-view. The qualitative evaluation methods are the dominant among all other methods. Most of the current computational methods are depending on unrealistic data sets, and/or applicable to certain types of algorithms. This, in turn, has led to the presence of unpopular and limited-use evaluation approaches. Certainly, this situation does not serve the goal of having standard, on-shelf methodologies that are able to quantify the performance of existing and future 3-D reconstruction techniques.; In this dissertation, we try to rectify this situation by proposing a unified computational framework for performance characterization of 3-D reconstruction techniques. The framework is three-fold. First, we introduce a new design for an experimental test-bed for the performance evaluation of 3-D reconstruction techniques. The setup integrates the functionality of 3-D laser scanners and CCD cameras. The setup provides accurate, general-use, automatically generated and registered dense ground truth data and their corresponding intensity data. The system bridges a gap in the evaluation research that is suffering from a lack of such data sets.; Second, we introduce a new 3-D registration technique dedicated to the evaluation problem. The 3-D registration is an important pre-evaluation request to get referenced evaluations. The proposed technique uses the image silhouettes instead of the actual 3-D reconstruction under-test. This makes the registration results independent of the quality of the reconstruction under-test. This feature is the major advantage of the proposed registration technique over the conventional techniques.; Third, we propose different computational evaluation methodologies and corresponding measuring criteria. These testing methodologies are independent of the 3-D reconstruction under-test. The methodologies are applied to the space carving technique as a common 3-D reconstruction technique to characterize its performance. Several concluding remarks on the space carving performance are provided.; Applications of the proposed framework other than performance tracking and diagnosis, as provided in the space carving case study, include system design and data fusion. We propose a draft design to a 3-D modeling vision system based on the evaluation provided for the space carving technique. Moreover, a method for data fusion of laser-based and camera-based reconstructions is presented.; We believe that presenting this framework to the computer vision community will help measure the progress in the 3-D modeling research and provide diagnosis tools for the current and the future 3-D reconstruction techniques. To maximize the benefits from this work, the data sets used throughout this research will be provided for the public use.