Samsung SMART CAMERA YouTube www.youtube.com Samsung SMART CAMERA's Real-Time Explorers take a visit to Cologne, for Photokina 2012. These lucky contestants were able to travel parts of Europe and blog about their adventures! Let's take a peek into their week. For more information about the camera & lenses please check the link below. www.samsung.com Shoot WOW, Share NOW! Samsung SMART CAMERA
Showing posts with label Realtime. Show all posts
Showing posts with label Realtime. Show all posts
Samsung SMART CAMERA Real-Time Explorers @ Photokina 2012
Posted Saturday, October 27, 2012 in Camera, Explorers, Photokina, Realtime, Samsung by Mary Rodriguez
Samsung SMART CAMERA Real-Time Explorers @ Photokina 2012 Video Clips. Duration : 2.52 Mins.
Samsung SMART CAMERA YouTube www.youtube.com Samsung SMART CAMERA's Real-Time Explorers take a visit to Cologne, for Photokina 2012. These lucky contestants were able to travel parts of Europe and blog about their adventures! Let's take a peek into their week. For more information about the camera & lenses please check the link below. www.samsung.com Shoot WOW, Share NOW! Samsung SMART CAMERA
Samsung SMART CAMERA YouTube www.youtube.com Samsung SMART CAMERA's Real-Time Explorers take a visit to Cologne, for Photokina 2012. These lucky contestants were able to travel parts of Europe and blog about their adventures! Let's take a peek into their week. For more information about the camera & lenses please check the link below. www.samsung.com Shoot WOW, Share NOW! Samsung SMART CAMERA
KinectFusion - Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera [28C3]
Posted Friday, October 12, 2012 in 28C3, Camera, Interaction, KinectFusion, Moving, Realtime, Reconstruction by Mary Rodriguez
KinectFusion - Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera [28C3] Video Clips. Duration : 66.52 Mins.
KinectFusion Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera This project investigates techniques to track the 6DOF position of handheld depth sensing cameras, such as Kinect, as they move through space and perform high quality 3D surface reconstructions for interaction. While depth cameras are not conceptually new, Kinect has made such sensors accessible to all. The quality of the depth sensing, given the low-cost and real-time nature of the device, is compelling, and has made the sensor instantly popular with researchers and enthusiasts alike. The Kinect camera uses a structured light technique to generate real-time depth maps containing discrete range measurements of the physical scene. This data can be reprojected as a set of discrete 3D points (or point cloud). Even though the Kinect depth data is compelling, particularly compared to other commercially available depth cameras, it is still inherently noisy. Depth mea- surements often fluctuate and depth maps contain numerous 'holes' where no readings were obtained. To generate 3D models for use in applications such as gaming, physics, or CAD, higher-level surface geometry needs to be inferred from this noisy point-based data. One simple approach makes strong assumptions about the connectivity of neighboring points within the Kinect depth map to generate a mesh representation. This, however, leads to noisy and low-quality meshes. As importantly, this approach creates an incomplete mesh, from ...
KinectFusion Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera This project investigates techniques to track the 6DOF position of handheld depth sensing cameras, such as Kinect, as they move through space and perform high quality 3D surface reconstructions for interaction. While depth cameras are not conceptually new, Kinect has made such sensors accessible to all. The quality of the depth sensing, given the low-cost and real-time nature of the device, is compelling, and has made the sensor instantly popular with researchers and enthusiasts alike. The Kinect camera uses a structured light technique to generate real-time depth maps containing discrete range measurements of the physical scene. This data can be reprojected as a set of discrete 3D points (or point cloud). Even though the Kinect depth data is compelling, particularly compared to other commercially available depth cameras, it is still inherently noisy. Depth mea- surements often fluctuate and depth maps contain numerous 'holes' where no readings were obtained. To generate 3D models for use in applications such as gaming, physics, or CAD, higher-level surface geometry needs to be inferred from this noisy point-based data. One simple approach makes strong assumptions about the connectivity of neighboring points within the Kinect depth map to generate a mesh representation. This, however, leads to noisy and low-quality meshes. As importantly, this approach creates an incomplete mesh, from ...
Subscribe to:
Posts (Atom)