Generation, visualization, and editing of 3D video

Matsuyama, Takashi; Takai, Takeshi

doi:10.1109/tdpvt.2002.1024068

Cited by 50 publications

(29 citation statements)

References 6 publications

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“…Texturing the reconstruction using the input images, allows us to obtain a 3D video [13] of the intervention if regulations allow it. Contrary to a regular video recording the viewer can choose his viewpoint freely, giving him more insight into the action.…”

Section: D Videomentioning

confidence: 99%

Real-Time 3D Reconstruction for Collision Avoidance in Interventional Environments

Ladikos

Benhimane

Navab

2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. With the increased presence of automated devices such as C-arms and medical robots and the introduction of a multitude of surgical tools, navigation systems and patient monitoring devices, collision avoidance has become an issue of practical value in interventional environments. In this paper, we present a real-time 3D reconstruction system for interventional environments which aims at predicting collisions by building a 3D representation of all the objects in the room. The 3D reconstruction is used to determine whether other objects are in the working volume of the device and to alert the medical staff before a collision occurs. In the case of C-arms, this allows faster rotational and angular movement which could for instance be used in 3D angiography to obtain a better reconstruction of contrasted vessels. The system also prevents staff to unknowingly enter the working volume of a device. This is of relevance in complex environments with many devices. The recovered 3D representation also opens the path to many new applications utilizing this data such as workflow analysis, 3D video generation or interventional room planning. To validate our claims, we performed several experiments with a real C-arm that show the validity of the approach. This system is currently being transferred to an interventional room in our university hospital.

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Section: D Videomentioning

confidence: 99%

Real-Time 3D Reconstruction for Collision Avoidance in Interventional Environments

Ladikos

Benhimane

Navab

2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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“…Shape-from-silhouette methods reconstruct geometry models of a scene from multi-view silhouette images or video streams. Examples are image-based [27,51] or polyhedral visual hull methods [26], as well as approaches performing point-based reconstruction [16]. The combination of stereo reconstruction with visual hull rendering leads to a more faithful reconstruction of surface concavities [25].…”

Section: Free-viewpoint Videomentioning

confidence: 99%

Video-based Capturing and Rendering of People

2008

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Abstract. The interesting combination of Chapters in this book gives a nice overview of the importance of the analysis of human motion in three different scientific disciplines, computer vision, biomechanics, and computer graphics. In the computer vision field the main goal has been to develop robust and efficient techniques for capturing and measuring human motion. Researchers from biomechanics, on the other hand, try to capitalize on such measurement methods to analyze the characteristics of human motion. In contrast, the focus of computer graphics research has been to realistically animate and render moving humans. In this Chapter, we will outline that a joint solution to the acquisition, the reconstruction, and the rendering problem paves the trail for video-based capturing and rendering of people. We present a model-based system to create realistic 3D videos of human actors that puts this idea into practice. Our method enables us to capture human actors with multiple video cameras and to render their performances in real-time from arbitrary novel viewpoints and under arbitrary novel lighting conditions.

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“…Shapefrom-silhouette methods reconstruct geometry models of a scene from multiview silhouette images or video streams [17], [18]. Starting from the silhouettes extracted from the camera pictures, a conservative shell enveloping the true geometry of the object is computed by reprojecting the silhouette cones into the 3-D scene and intersecting them.…”

Section: -D Videomentioning

confidence: 99%

“…There exist two classes of methods to compute the visual hull: 1) voxel carving methods, which carve away all voxels that are not contained in the silhouettes of the acquisition cameras and 2) image-based methods that exploit epipolar geometry and store so-called occupancy intervals at every pixel. Some examples are image-based [18] or polyhedral visual hull methods [17] as well as approaches performing point-based reconstruction [10]. Despite quality limitations of the measured shape models, visual hull reconstruction methods are still the algorithms of choice when real-time performance is the primary goal.…”

Section: -D Videomentioning

confidence: 99%