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.

Texture has long been regarded as spatial distributions of gray-level variation, and texture analysis has generally been confined to the 2-D image domain. Introducing the concept of "3-D world feature", this paper considers texture as a function of 3-D structures and proposes a set of "3-D textural features". The proposed 3-D features appear to have a great potential in terrain classification. Experiments have been carried out to compare the 3-D features with a popular traditional 2-D feature set. The results show that the 3-D features significantly outperform the 2-D features in terms of classification accuracy.

A system has been developed to acquire, extend and refine 3D geometric site models from aerial imagery. This system hypothesizes potential building roofs in an image, automatically locates supporting geometric evidence in other images, and determines the precise shape and position of the new buildings via multiimage triangulation. Model-to-image registration techniques are applied to align new, incoming images against the site model. Model extension and refinement procedures are then performed to add previously unseen buildings and to improve the geometric accuracy of the existing 3D building models.>

A new method for extracting planar polygonal rooftops in monocular aerial imagery is proposed. Structural features are extracted and hierarchically related using perceptual grouping techniques. Top-down feature verification is used so that features, and links between the features, are verified with local information in the image and weighed in a graph. Cycles in the graph correspond to possible building rooftop hypotheses. Virtual features are hypothesized for the perceptual completion of partially occluded rooftops. Extraction of the "best" grouping features into a building rooftop hypothesis is posed as a graph search problem. The maximally weighted, independent set of cycles in the graph is extracted as the final set of roof boundaries.>