Efficient reconstruction of nonrigid shape and motion from real-time 3D scanner data
ACM Transactions on Graphics (TOG)
Non-rigid shape matching using geometry and photometry
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Globally optimal spatio-temporal reconstruction from cluttered videos
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Animation cartography—intrinsic reconstruction of shape and motion
ACM Transactions on Graphics (TOG)
Dense and accurate spatio-temporal multi-view stereovision
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Keypoints and Local Descriptors of Scalar Functions on 2D Manifolds
International Journal of Computer Vision
Non-rigid 3D shape tracking from multiview video
Computer Vision and Image Understanding
On the evaluation of scene flow estimation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Estimating scene flow using an interconnected patch surface model with belief-propagation inference
Computer Vision and Image Understanding
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In this paper, we address the problem of surface tracking in multiple camera environments and over time sequences. In order to fully track a surface undergoing significant deformations, we cast the problem as a mesh evolution over time. Such an evolution is driven by 3D displacement fields estimated between meshes recovered independently at different time frames. Geometric and photometric information is used to identify a robust set of matching vertices. This provides a sparse displacement field that is densified over the mesh by Laplacian diffusion. In contrast to existing approaches that evolve meshes, we do not assume a known model or a fixed topology. The contribution is a novel mesh evolution based framework that allows to fully track, over long sequences, an unknown surface encountering deformations, including topological changes. Results on very challenging and publicly available image based 3D mesh sequences demonstrate the ability of our framework to efficiently recover surface motions .