Adaptive Structure from Motion with a Contrario Model Estimation

Moulon, Pierre; Monasse, Pascal; Marlet, Renaud

doi:10.1007/978-3-642-37447-0_20

Cited by 162 publications

(125 citation statements)

References 31 publications

(32 reference statements)

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“…In this setting, line and possibly point inliers are automatically selected without having to set specific thresholds. Moreover, Moulon et al [23] have shown that such a parameterless AC-RANSAC performs better than standard RANSAC, not only because it relies on optimal thresholds but also because it can adapt to data variation within a single dataset.…”

Section: Robust Parameterless a Contrario Pose Estimationmentioning

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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Section: Robust Parameterless a Contrario Pose Estimationmentioning

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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“…Using all previous modules, an incremental [3] and a global [5] 3D-reconstruction pipelines are implemented in OpenMVG. The first is more adapted for images with low cross-coverage, but it suffers from drift effects and low scalability due to its sequential nature.…”

Section: Structure From Motionmentioning

confidence: 99%

OpenMVG: Open Multiple View Geometry

Moulon

Monasse

Perrot

et al. 2017

Lecture Notes in Computer Science

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286

251

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Abstract. The OpenMVG C++ library provides a vast collection of multipleview geometry tools and algorithms to spread the usage of computer vision and structure-from-motion techniques. Close to the state-of-the-art in its domain, it provides an easy access to common tools used in 3D reconstruction from images. Following the credo "Keep it simple, keep it maintainable" the library is designed as a modular collection of algorithms, libraries and binaries that can be used independently or as bricks to build larger systems. Thanks to its strict test driven development, the library is packaged with unit-test code samples that make the library easy to learn, modify and use. Since its first release in 2013 under the MPL2 license, OpenMVG has gathered an active community of users and contributors from many fields, spanning hobbyists, students, computer vision experts, and industry members.

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“…Over the last decade, structure-from-motion (SfM) and dense multi-view stereo (MVS) reconstruction have benefited from constant progress in feature detection and matching, and camera calibration, leading to mature systems, e.g, Bundler [36,35], VisualSFM [41,42], openMVG [25,24,26], PMVS-2 [10], CMP-MVS [14], including consumer products such as Acute3D ContextCapture and Agisoft PhotoScan. Current state-of-the-art methods are now able to produce impressive 3D reconstructions for many scene categories with a rich level of detail, assuming there are enough input images and the scene is sufficiently textured.…”

Section: Introductionmentioning

confidence: 99%

Patchwork Stereo: Scalable, Structure-Aware 3D Reconstruction in Man-Made Environments

Bourki

Gorce

Marlet

et al. 2017

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

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In this paper, we address the problem of Multi-View Stereo (MVS) reconstruction of highly regular man-made scenes from calibrated, wide-baseline views and a sparse Structure-from-Motion (SfM) point cloud. We introduce a novel patch-based formulation via energy minimization which combines top-down segmentation hypotheses using appearance and vanishing line detections, as well as an arrangement of creased planar structures which are extracted automatically through a robust analysis of available SfM points and image features. The method produces a compact piecewise-planar depth map and a mesh which are aligned with the scene's structure. Experiments show that our approach not only reaches similar levels of accuracy w.r.t state-of-the-art pixel-based methods while using much fewer images, but also produces a much more compact, structure-aware mesh in a considerably shorter runtime by several of orders of magnitude.

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Adaptive Structure from Motion with a Contrario Model Estimation

Cited by 162 publications

References 31 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

OpenMVG: Open Multiple View Geometry

Patchwork Stereo: Scalable, Structure-Aware 3D Reconstruction in Man-Made Environments

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