Motion graphs for unstructured textured meshes

Prada, Fabián; Kazhdan, Michael; Chuang, Ming; Collet, Alvaro; Hoppe, Hugues

doi:10.1145/2897824.2925967

Cited by 35 publications

(36 citation statements)

References 35 publications

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“…Optical flow Though initially proposed for aligning images [LK81], optical flow has been extended to the sphere [YSV*10] and more general surfaces [PKC*16]. These extensions formulate the brightness constancy constraint in terms of local tangent spaces and iteratively update the deformation by using temporally varying tangent vectors fields and exponentiation to parametrize the change in deformation.…”

Section: Related Workmentioning

confidence: 99%

“…Using the approach of Prada et al . [PKC*16], we find W⃗ hierarchically by using the Lie‐derivative on 0‐forms to linearize advection:…”

Section: Our Approachmentioning

confidence: 99%

“…In a pre‐processing step we conformally map each shape to the sphere [KSBC12], center it so that the scale‐factor‐weighted center of mass is at the origin [BCK18], and evolve the parametrization to reduce the extreme area scaling at extremities [ZHG11] (Figure , top). Then, at run‐time, we process pairs of shapes by first solving for the optimal rotation that registers the two spherical parametrizations [BCK18] and then refining the rigid registration by computing the optical flow advecting the parametrization towards each other [PKC*16] (Figure , bottom).…”

Section: Introductionmentioning

confidence: 99%

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Dense Point‐to‐Point Correspondences Between Genus‐Zero Shapes

Lee

Kazhdan

2019

Computer Graphics Forum

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We describe a novel approach that addresses the problem of establishing correspondences between non‐rigidly deformed shapes by performing the registration over the unit sphere. In a pre‐processing step, each shape is conformally parametrized over the sphere, centered to remove Möbius inversion ambiguity, and authalically evolved to expand regions that are excessively compressed by the conformal parametrization. Then, for each pair of shapes, we perform fast SO(3) correlation to find the optimal rotational alignment and refine the registration using optical flow. We evaluate our approach on the TOSCA dataset, demonstrating that our approach compares favorably to state‐of‐the‐art methods.

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Section: Related Workmentioning

confidence: 99%

“…Using the approach of Prada et al . [PKC*16], we find W⃗ hierarchically by using the Lie‐derivative on 0‐forms to linearize advection:…”

Section: Our Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dense Point‐to‐Point Correspondences Between Genus‐Zero Shapes

Lee

Kazhdan

2019

Computer Graphics Forum

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“…Gauss map diffusion (or spherical harmonic flow) is discussed in [PKC*16], and normal field filtering is also exploited in [Tau01, YOB02, TWBO02, TWBO03, SRML07, ZFAT11]. However, they are only used in the application of mesh smoothing and denoising.…”

Section: Related Workmentioning

confidence: 99%

Mesh Parametrization Driven by Unit Normal Flow

Zhao

et al. 2019

Computer Graphics Forum

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Based on mesh deformation, we present a unified mesh parametrization algorithm for both planar and spherical domains. Our approach can produce intermediate frames from the original meshes to the targets. We derive and define a novel geometric flow: ‘unit normal flow (UNF)’ and prove that if UNF converges, it will deform a surface to a constant mean curvature (CMC) surface, such as planes and spheres. Our method works by deforming meshes of disk topology to planes, and spherical meshes to spheres. Our algorithm is robust, efficient, simple to implement. To demonstrate the robustness and effectiveness of our method, we apply it to hundreds of models of varying complexities. Our experiments show that our algorithm can be a competing alternative approach to other state‐of‐the‐art mesh parametrization methods. The unit normal flow also suggests a potential direction for creating CMC surfaces.

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“…Sequential methods for frame-to-frame temporal coherence are prone to errors due to drift and rapid motion [6,46]. Previous work [54] has shown that semantic tracklets improve segmentation for single view video.…”

Section: Semantic Trackletsmentioning

confidence: 99%

Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes

Mustafa

Hilton

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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In this paper we propose a framework for spatially and temporally coherent semantic co-segmentation and reconstruction of complex dynamic scenes from multiple static or moving cameras. Semantic co-segmentation exploits the coherence in semantic class labels both spatially, between views at a single time instant, and temporally, between widely spaced time instants of dynamic objects with similar shape and appearance. We demonstrate that semantic coherence results in improved segmentation and reconstruction for complex scenes. A joint formulation is proposed for semantically coherent object-based co-segmentation and reconstruction of scenes by enforcing consistent semantic labelling between views and over time. Semantic tracklets are introduced to enforce temporal coherence in semantic labelling and reconstruction between widely spaced instances of dynamic objects. Tracklets of dynamic objects enable unsupervised learning of appearance and shape priors that are exploited in joint segmentation and reconstruction. Evaluation on challenging indoor and outdoor sequences with hand-held moving cameras shows improved accuracy in segmentation, temporally coherent semantic labelling and 3D reconstruction of dynamic scenes.

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Motion graphs for unstructured textured meshes

Cited by 35 publications

References 35 publications

Dense Point‐to‐Point Correspondences Between Genus‐Zero Shapes

Dense Point‐to‐Point Correspondences Between Genus‐Zero Shapes

Mesh Parametrization Driven by Unit Normal Flow

Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes

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