Spatio-Temporally Consistent Correspondence for Dense Dynamic Scene Modeling

Ji, Dinghuang; Dunn, Enrique; Frahm, Jan

doi:10.1007/978-3-319-46466-4_1

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…They take into account data, contrast, smoothness, and temporal characteristics to narrow the solution space and achieve a clean depth restoration for multiple synchronized and unsynchronized input videos. In contrast, Ji et al presented a method for the 3D reconstruction of dynamic scene objects based on video synchronization that exploits locally rigid patches without the need for segmentation [ 9 ]. However, these approaches are limited to a few large, moving foreground objects in a scene.…”

Section: Related Workmentioning

confidence: 99%

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

Kraus

Pollok

Miller

et al. 2020

Sensors

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The technical progress in the last decades makes photo and video recording devices omnipresent. This change has a significant impact, among others, on police work. It is no longer unusual that a myriad of digital data accumulates after a criminal act, which must be reviewed by criminal investigators to collect evidence or solve the crime. This paper presents the VICTORIA Interactive 4D Scene Reconstruction and Analysis Framework (“ISRA-4D” 1.0), an approach for the visual consolidation of heterogeneous video and image data in a 3D reconstruction of the corresponding environment. First, by reconstructing the environment in which the materials were created, a shared spatial context of all available materials is established. Second, all footage is spatially and temporally registered within this 3D reconstruction. Third, a visualization of the hereby created 4D reconstruction (3D scene + time) is provided, which can be analyzed interactively. Additional information on video and image content is also extracted and displayed and can be analyzed with supporting visualizations. The presented approach facilitates the process of filtering, annotating, analyzing, and getting an overview of large amounts of multimedia material. The framework is evaluated using four case studies which demonstrate its broad applicability. Furthermore, the framework allows the user to immerse themselves in the analysis by entering the scenario in virtual reality. This feature is qualitatively evaluated by means of interviews of criminal investigators and outlines potential benefits such as improved spatial understanding and the initiation of new fields of application.

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Section: Related Workmentioning

confidence: 99%

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

Kraus

Pollok

Miller

et al. 2020

Sensors

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show abstract

“…For completeness, image regions are reconstructed independently using shape basis [41] or local patches [22], which can be stitched together to form complete scene reconstruction. Further, the spatial and temporal priors can be combined to produce dense correspondences, resulting in complete 4D reconstruction [17]. Humans are a special case of spatial constraints, which allow markerless motion capture from a monocular camera [52,35,2].…”

Section: Related Workmentioning

confidence: 99%

Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera

Jae¹,

Kim²,

Gallo³

et al. 2020

Preprint

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Figure 1: Dynamic Scene View Synthesis: (Left) A dynamic scene is captured from a monocular camera from the locations V 0 to V k . Each image captures people jumping at each time step (t = 0 to t = k). (Middle) A novel view from an arbitrary location between V 0 and V 1 (denoted as an orange frame) is synthesized with the dynamic contents observed at the time t = k. The estimated depth at V k is shown in the inset. (Right) For the novel view (orange frame), we can also synthesize the dynamic content that appeared across any views in different time (traces of the foreground in each time step are shown). More results are shown in Sec. 5 and the supplementary document and video.

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“…Tuytelaars et al [34] determined sequencing based on the approximate 3D intersections of viewing rays under an affine reference frame. Ji et al [19] jointly synchronize a pair of video sequences and reconstruct their commonly observed dense 3D structure by maximizing the spatio-temporal consistency of two-view pixel correspondences across video sequences.…”

Section: Related Workmentioning

confidence: 99%

Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction

Dunn

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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We present a general paradigm for dynamic 3D reconstruction from multiple independent and uncontrolled image sources having arbitrary temporal sampling density and distribution. Our graph-theoretic formulation models the spatio-temporal relationships among our observations in terms of the joint estimation of their 3D geometry and its discrete Laplace operator. Towards this end, we define a tri-convex optimization framework that leverages the geometric properties and dependencies found among a Euclidean shape-space and the discrete Laplace operator describing its local and global topology. We present a reconstructability analysis, experiments on motion capture data and multi-view image datasets, as well as explore applications to geometrybased event segmentation and data association.

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Spatio-Temporally Consistent Correspondence for Dense Dynamic Scene Modeling

Cited by 7 publications

References 23 publications

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

Toward Mass Video Data Analysis: Interactive and Immersive 4D Scene Reconstruction

Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera

Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction

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