The ability to efficiently and robustly recover accurate 3D terrain models from sets of stereoscopic images is important to many civilian and military applications. Our long-term goal is to develop an automatic, multi-image 3D reconstruction algorithm that can be applied to these domains. To develop an effective and practical terrain modeling system, methods must be found for detecting unreliable elevations in digital elevation maps (DEMs), and for fusing several DEMs from multiple sources into an accurate and reliable result. This paper focuses on two key factors for generating robust 3D terrain models, (1) the ability to detect unreliable elevations estimates, and (2) to fuse the reliable elevations into a single optimal terrain model. The techniques discussed in this paper are based on the concept of using self-consistency to identify potentially unreliable points.We apply the self-consistency methodology to both the two-image and multi-image scenarios. We demonstrate that the recently developed concept of self-consistency can be effectively employed to determine the reliability of values in a DEM. Estimates with a reliability below an error threshold can be excluded from further processing.We test the effectiveness of the methodology, as well as the relationship between error rate and scene geometry by processing both real and photo-realistic simulations..

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This paper presents a 3D camera calibration method based on a nonlinear modeling function of an artificial neural network. The neural network employed in this paper is primarily used as a nonlinear mapper between 2D image points and points of a certain space in 3D real world. The neural network model implicitly contains all the physical parameters, some of which are very difficult to be estimated in the conventional calibration methods. MutiLayer Perceptron Type Neural Network (MLPNN) is employed to implement the relationship between image coordinates. In order to show the performance of the proposed method, we carry out experiments on the estimation of 2D image coordinates given 3D real world coordinates. The experimental results show that the proposed method improved calibration accuracy over widely used Tsai's two stage method (TSM).