2-manifold reconstruction from sparse visual features

Литвинов, В. В.; Yu, Shuda; Lhuillier, Maxime

doi:10.1109/ic3d.2012.6615134

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…A smaller number of techniques correlate and reconstruct image curvilinear structure across views, resulting in 3D curvilinear structure. Pipelines based on straight lines (see [20,31,11] for recent reviews), algebraic and general curve features [29,21,10,8,23,36,9] have been proposed, but some lack generality, e.g., requiring specific curve models [26]. The 3D Curve Sketch system [7,8,10] operates on multiple views by pairing curves from two arbitrary "hypothesis views" at a time via epipolar-geometric consistency.…”

Section: Introductionmentioning

confidence: 99%

From Multiview Image Curves to 3D Drawings

Usumezbas

Fabbri

Kimia

2016

Computer Vision – ECCV 2016

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Abstract. Reconstructing 3D scenes from multiple views has made impressive strides in recent years, chiefly by correlating isolated feature points, intensity patterns, or curvilinear structures. In the general setting -without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity -most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing unorganized clouds of 3D curve fragments. Ideally, many applications require structured representations of curves, surfaces and their spatial relationships. This paper presents a step in this direction by formulating an approach that combines 2D image curves into a collection of 3D curves, with topological connectivity between them represented as a 3D graph. This results in a 3D drawing, which is complementary to surface representations in the same sense as a 3D scaffold complements a tent taut over it. We evaluate our results against truth on synthetic and real datasets.

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Section: Introductionmentioning

confidence: 99%

From Multiview Image Curves to 3D Drawings

Usumezbas

Fabbri

Kimia

2016

Computer Vision – ECCV 2016

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“…Third, some methods employ curve differential geometry in correlating structure across views. Complete 3D reconstruction pipelines based on straight lines (Lebeda et al 2014;Zhang 2013;Fathi et al 2015), algebraic and general curve features (Teney and Piater 2012;Litvinov et al 2012;Fabbri and Kimia 2010;Fabbri et al 2012;Pötsch and Pinz 2011) have been proposed. The compact curvebased 3D representation that has found demand in seveal tasks: fast recognition of general 3D scenery (Pötsch and Pinz 2011), efficient transmission of general 3D scenes, scene understanding and modeling by reasoning at junctions (Mattingly et al 2015), consistent non-photorealistic rendering from video (Chen and Klette 2014), modeling of branching structures, to name a few (Rao et al 2012;Kowdle et al 2012;Wang et al 2014).…”

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confidence: 99%

Multiview Differential Geometry of Curves

Fabbri

Kimia

2016

Int J Comput Vis

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The field of multiple view geometry has seen tremendous progress in reconstruction and calibration due to methods for extracting reliable point features and key developments in projective geometry. Point features, however, are not available in certain applications and result in unstructured point cloud reconstructions. General image curves provide a complementary feature when keypoints are scarce, and result in 3D curve geometry, but face challenges not addressed by the usual projective geometry of points and algebraic curves. We address these challenges by laying the theoretical foundations of a framework based on the differential geometry of general curves, including stationary curves, occluding contours, and non-rigid curves, aiming at stereo correspondence, camera estimation (including calibration, pose, and multiview epipolar geometry), and 3D reconstruction given measured image curves. By gathering previous results into a cohesive theory, novel results were made possible, yielding three contributions. First we derive the differential geometry of an image curve (tangent, curvature, curvature derivative) from that of the underlying space curve (tangent, curvature, curvature derivative, torsion). Second, we derive the differential geometry of a space curve from that of two corresponding image curves. Third, the differential motion of an image curve is derived from camera motion and the differential geometry and motion of the space curve. The availability of such a theory enables novel curve-based multiview reconstruction and camera estimation systems to augment existing point-based approaches. This theory has been used to reconstruct a "3D curve sketch", to determine camera pose from local curve geometry, and tracking; other developments are underway.Comment: International Journal of Computer Vision Final Accepted version. International Journal of Computer Vision, 2016. The final publication is available at Springer via http://dx.doi.org/10.1007/s11263-016-0912-

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Towards Pointless Structure from Motion: 3D Reconstruction and Camera Parameters from General 3D Curves

Nurutdinova

Fitzgibbon

2015

2015 IEEE International Conference on Computer Vision (ICCV)

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2-manifold reconstruction from sparse visual features

Cited by 4 publications

References 18 publications

From Multiview Image Curves to 3D Drawings

From Multiview Image Curves to 3D Drawings

Multiview Differential Geometry of Curves

Towards Pointless Structure from Motion: 3D Reconstruction and Camera Parameters from General 3D Curves

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