3D Reconstruction of Dynamic Textures in Crowd Sourced Data

Ji, Dinghuang; Dunn, Enrique; Frahm, Jan‐Michael

doi:10.1007/978-3-319-10590-1_10

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…The system then creates a cinemagraph rendering by only animating those regions while fixing other pixels to the reference frame (right). been recently proposed [12]. They jointly reconstruct the static background and dynamic foreground objects.…”

Section: Related Workmentioning

confidence: 99%

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Turning an Urban Scene Video into a Cinemagraph

Yan

Liu

Furukawa

2017

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

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This paper proposes an algorithm that turns a regular video capturing urban scenes into a high-quality endless animation, known as a Cinemagraph. The creation of a Cinemagraph usually requires a static camera in a carefully configured scene. The task becomes challenging for a regular video with a moving camera and objects. Our approach first warps an input video into the viewpoint of a reference camera. Based on the warped video, we propose effective temporal analysis algorithms to detect regions with static geometry and dynamic appearance, where geometric modeling is reliable and visually attractive animations can be created. Lastly, the algorithm applies a sequence of video processing techniques to produce a Cinemagraph movie. We have tested the proposed approach on numerous challenging real scenes. To our knowledge, this work is the first to automatically generate Cinemagraph animations from regular movies in the wild.

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Section: Related Workmentioning

confidence: 99%

“…been recently proposed [12]. They jointly reconstruct the static background and dynamic foreground objects.…”

Section: Related Workmentioning

confidence: 99%

Turning an Urban Scene Video into a Cinemagraph

Yan

Liu

Furukawa

2017

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

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“…The input is a partial surface reconstruction or depth map of a general dynamic scenes at each frame together with single or multiple view images. Cameras may be static or moving and camera calibration is assumed to be known or estimated together with the scene reconstruction [31,32,3,20]. The first step is to estimate sparse wide-timeframe feature correspondence.…”

Section: Overviewmentioning

confidence: 99%

4D Match Trees for Non-rigid Surface Alignment

Mustafa

Kim

Hilton

2016

Computer Vision – ECCV 2016

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Abstract. This paper presents a method for dense 4D temporal alignment of partial reconstructions of non-rigid surfaces observed from single or multiple moving cameras of complex scenes. 4D Match Trees are introduced for robust global alignment of non-rigid shape based on the similarity between images across sequences and views. Wide-timeframe sparse correspondence between arbitrary pairs of images is established using a segmentation-based feature detector (SFD) which is demonstrated to give improved matching of non-rigid shape. Sparse SFD correspondence allows the similarity between any pair of image frames to be estimated for moving cameras and multiple views. This enables the 4D Match Tree to be constructed which minimises the observed change in non-rigid shape for global alignment across all images. Dense 4D temporal correspondence across all frames is then estimated by traversing the 4D Match tree using optical flow initialised from the sparse feature matches. The approach is evaluated on single and multiple view images sequences for alignment of partial surface reconstructions of dynamic objects in complex indoor and outdoor scenes to obtain a temporally consistent 4D representation. Comparison to previous 2D and 3D scene flow demonstrates that 4D Match Trees achieve reduced errors due to drift and improved robustness to large non-rigid deformations.

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“…Other approaches for reconstruction of general scenes from multiple handheld wide-baseline cameras [3,41] exploit prior reconstruction of the background scene to allow dynamic foreground segmentation and reconstruction. Recent approaches for spatio-temporal reconstruction of multi-view data either work on indoor studio data [35] or for dynamic reconstruction of crowd sourced data [24]. Methods to estimate 3D scene flow have been reported in the literature [31].…”

mentioning

confidence: 99%

Temporally Coherent 4D Reconstruction of Complex Dynamic Scenes

Mustafa

Kim

Guillemaut

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras. Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects. Temporal coherence is exploited to overcome visual ambiguities resulting in improved reconstruction of complex scenes. Robust joint segmentation and reconstruction of dynamic objects is achieved by introducing a geodesic star convexity constraint. Comparative evaluation is performed on a variety of unstructured indoor and outdoor dynamic scenes with hand-held cameras and multiple people. This demonstrates reconstruction of complete temporally coherent 4D scene models with improved nonrigid object segmentation and shape reconstruction.

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3D Reconstruction of Dynamic Textures in Crowd Sourced Data

Cited by 9 publications

References 16 publications

Turning an Urban Scene Video into a Cinemagraph

Turning an Urban Scene Video into a Cinemagraph

4D Match Trees for Non-rigid Surface Alignment

Temporally Coherent 4D Reconstruction of Complex Dynamic Scenes

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