Detection of Planar Regions with Uncalibrated Stereo using Distributions of Feature Points

Kanazawa, Yasushi; Kawakami, Hiroshi

doi:10.5244/c.18.27

Cited by 101 publications

(68 citation statements)

References 16 publications

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“…To increase the likelihood that the points belong to the same plane they select points lying on two different lines in an image. In contrast Kanazawa et al [9] define a probability for feature points to belong to the same plane using the Euclidean distance between the points. Both approaches use a RANSAC scheme, iteratively detect the dominant plane, remove the inliers and precede with the remaining interest points.…”

Section: Related Workmentioning

confidence: 99%

Incremental Model Selection for Detection and Tracking of Planar Surfaces

Prankl

Zillich

Leibe

et al. 2010

Procedings of the British Machine Vision Conference 2010

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Man-made environments are abundant with planar surfaces which have attractive properties and are a prerequisite for a variety of vision tasks. This paper presents an incremental model selection method to detect piecewise planar surfaces, where planes once detected are tracked and serve as priors in subsequent images. The novelty of this approach is to formalize model selection for plane detection with Minimal Description Length (MDL) in an incremental manner. In each iteration tracked planes and new planes computed from randomly sampled interest points are evaluated, the hypotheses which best explain the scene are retained, and their supporting points are marked so that in the next iteration random sampling is guided to unexplained points. Hence, the remaining finer scene details can be represented. We show in a quantitative evaluation that this new method competes with state of the art algorithms while it is more flexible to incorporate prior knowledge from tracking.

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Section: Related Workmentioning

confidence: 99%

Incremental Model Selection for Detection and Tracking of Planar Surfaces

Prankl

Zillich

Leibe

et al. 2010

Procedings of the British Machine Vision Conference 2010

View full text Add to dashboard Cite

show abstract

“…Examples include sequential RANSAC [32,15], multi-RANSAC [35] , FLoSS [18], and CC-RANSAC [8]. An extensive survey of RANSAC-based methods can be found in [24].…”

Section: Previous Workmentioning

confidence: 99%

Multi-planar Fitting in an Indoor ManhattanWorld

Kim

Manduchi

2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

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We present an algorithm that finds planar structures in a Manhattan world from two pictures taken from different viewpoints with unknown baseline. The Manhattan world assumption constrains the homographies induced by the visible planes on the image pair, thus enabling robust reconstruction. We extend the T-linkage algorithm for multistructure discovery to account for constrained homographies, and introduce algorithms for sample point selection and orientation-preserving cluster merging. Results are presented on three indoor data set, showing the benefit of the proposed constraints and algorithms.

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“…A consensus matrix P has to be instantiated via random sampling, but we are agnostic here on the specific sampling strategy (e.g. [27,28,17]). At first we concentrate on the case in which all the points are inliers (the case of outliers will be dealt with later on).…”

Section: Set Cover Formulationmentioning

confidence: 99%

Multiple structure recovery with maximum coverage

Magri

Fusiello

2017

Machine Vision and Applications

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We present a general framework for geometric model fitting based on a set coverage formulation, that caters for intersecting structures and outliers in a simple and principled manner. The multi-model fitting problem is formulated in terms of the optimization of a consensus-based global cost function, which allows to sidestep the pitfalls of preference approaches based on clustering and to avoid the difficult trade-off between data fidelity and complexity of other optimization formulations. Two especially appealing characteristics of this method are the ease with which it can be implemented and its modularity with respect to the solver and to the sampling strategy. Few intelligible parameters need to be set and tuned, namely the inlier threshold and the number of desired models. The summary of the experiments is that our method compares favourably with its competitors overall, and it is always either the best performer or almost on par with the best performer in specific scenarios.

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Detection of Planar Regions with Uncalibrated Stereo using Distributions of Feature Points

Cited by 101 publications

References 16 publications

Incremental Model Selection for Detection and Tracking of Planar Surfaces

Incremental Model Selection for Detection and Tracking of Planar Surfaces

Multi-planar Fitting in an Indoor ManhattanWorld

Multiple structure recovery with maximum coverage

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