Wide-Baseline Stereo Matching with Line Segments

Bay, Herbert; Ferraris, Vinicius; Gool, Luc Van

doi:10.1109/cvpr.2005.375

Cited by 174 publications

(134 citation statements)

References 14 publications

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“…Another family of approaches use junctions at line intersections, reducing to a point problem [14,15]. However, as shown below, these points are not accurately located.…”

Section: Related Workmentioning

confidence: 99%

Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions

Salaün

Marlet

Monasse

2016

Computer Vision – ECCV 2016

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Abstract. Usual Structure from Motion techniques based on feature points have a hard time on scenes with little texture or presenting a single plane, as in indoor environments. Line segments are more robust features in this case. We propose a novel geometrical criterion for two-view pose estimation using lines, that does not assume a Manhattan world. We also define a parameterless (a contrario) RANSAC-like method to discard calibration outliers and provide more robust pose estimations, possibly using points as well when available. Last, we provide quantitative experimental data that illustrate failure cases of other methods and that show how our approach outperforms them, both in robustness and accuracy.

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“…Another family of approaches use junctions at line intersections, reducing to a point problem [14,15]. However, as shown below, these points are not accurately located.…”

Section: Related Workmentioning

confidence: 99%

Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions

Salaün

Marlet

Monasse

2016

Computer Vision – ECCV 2016

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“…Overall, most of these methods are not adapted to satellite images whose wide baselines typically produce severe occlusion problems that are not specifically handled. The additional use of geometric primitives as line-segments usually helps to better interpret occluded parts of images [20].…”

Section: Related Workmentioning

confidence: 99%

Towards Large-Scale City Reconstruction from Satellites

Duan

Lafarge

2016

Computer Vision – ECCV 2016

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Abstract. Automatic city modeling from satellite imagery is one of the biggest challenges in urban reconstruction. Existing methods produce at best rough and dense Digital Surface Models. Inspired by recent works on semantic 3D reconstruction and region-based stereovision, we propose a method for producing compact, semantic-aware and geometrically accurate 3D city models from stereo pair of satellite images. Our approach relies on two key ingredients. First, geometry and semantics are retrieved simultaneously bringing robustness to occlusions and to low image quality. Second, we operate at the scale of geometric atomic region which allows the shape of urban objects to be well preserved, and a gain in scalability and efficiency. We demonstrate the potential of our algorithm by reconstructing different cities around the world in a few minutes.

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“…The mapping constraints applied here are the sidedness constraint [25] and the one-to-one constraint.…”

Section: Problem-solving Using the Spectral Techniquementioning

confidence: 99%

“…Many methods of matching individual line segments are based on the appearance similarities of the line segments, such as [25], in which colour histograms of neighbouring profiles are used to obtain an original set of candidate matches that grows iteratively. In [26], the author proposed a mean standard deviation line descriptor (MSLD), based on the appearance of the pixel support region.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Initial Position Using Line Segment Matching in Maps

Wei

Wang

et al. 2016

International Journal of Advanced Robotic Systems

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While navigating in a typical traffic scene, with a drastic drift or sudden jump in its Global Positioning System (GPS) position, the localization based on such an initial position is unable to extract precise overlapping data from the prior map in order to match the current data, thus rendering the localization as unfeasible. In this paper, we first propose a new method to estimate an initial position by matching the infrared reflectivity maps. The maps consist of a highly precise prior map, built with the offline simultaneous localization and mapping (SLAM) technique, and a smooth current map, built with the integral over velocities. Considering the attributes of the maps, we first propose to exploit the stable, rich line segments to match the lidar maps. To evaluate the consistency of the candidate line pairs in both maps, we propose to adopt the local appearance, pairwise geometric attribute and structural likelihood to construct an affinity graph, as well as employ a spectral algorithm to solve the graph efficiently. The initial position is obtained according to the relationship between the vehicle's current position and matched lines. Experiments on the campus with a GPS error of dozens of metres show that our algorithm can provide an accurate initial value with average longitudinal and lateral errors being 1.68m and 1.04m, respectively.

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Wide-Baseline Stereo Matching with Line Segments

Cited by 174 publications

References 14 publications

Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions

Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions

Towards Large-Scale City Reconstruction from Satellites

Estimation of Initial Position Using Line Segment Matching in Maps

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