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We present an extensive evaluation of 17 confidence measures for stereo matching that compares the most widely used measures as well as several novel techniques proposed here. We begin by categorizing these methods according to which aspects of stereo cost estimation they take into account and then assess their strengths and weaknesses. The evaluation is conducted using a winner-take-all framework...
We present a method for detecting and parsing buildings from unorganized 3D point clouds into a compact, hierarchical representation that is useful for high-level tasks. The input is a set of range measurements that cover large-scale urban environment. The desired output is a set of parse trees, such that each tree represents a semantic decomposition of a building - the nodes are roof surfaces as...
We revisit stereo matching functions, a topic that is considered well understood, from a different angle. Our goal is to discover a transformation that operates on the cost or similarity measures between pixels in binocular stereo. This transformation should produce a new matching curve that results in higher matching accuracy. The desired transformation must have no additional parameters over those...
We address the problem of extracting the road network from large-scale range datasets. Our approach is fully automatic and does not require any inputs other than depth and intensity measurements from the range sensor. Road extraction is important because it provides contextual information for scene analysis and enables automatic content generation for geographic information systems (GIS). In addition...
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