tailieunhanh - Multi-view stereo

To help you specialized Electronics Electronics has added references in the process of learning and study. Invite you to consult the lecture content "Multi-view stereo". Each of your content and references for additional lectures will serve the needs of learning and research. | Multi-view stereo Many slides adapted from S. Seitz What is stereo vision? Generic problem formulation: given several images of the same object or scene, compute a representation of its 3D shape What is stereo vision? Generic problem formulation: given several images of the same object or scene, compute a representation of its 3D shape “Images of the same object or scene” Arbitrary number of images (from two to thousands) Arbitrary camera positions (isolated cameras or video sequence) Cameras can be calibrated or uncalibrated “Representation of 3D shape” Depth maps Meshes Point clouds Patch clouds Volumetric models Layered models The third view can be used for verification Beyond two-view stereo Pick a reference image, and slide the corresponding window along the corresponding epipolar lines of all other images, using inverse depth relative to the first image as the search parameter M. Okutomi and T. Kanade, “A Multiple-Baseline Stereo System,” IEEE Trans. on . | Multi-view stereo Many slides adapted from S. Seitz What is stereo vision? Generic problem formulation: given several images of the same object or scene, compute a representation of its 3D shape What is stereo vision? Generic problem formulation: given several images of the same object or scene, compute a representation of its 3D shape “Images of the same object or scene” Arbitrary number of images (from two to thousands) Arbitrary camera positions (isolated cameras or video sequence) Cameras can be calibrated or uncalibrated “Representation of 3D shape” Depth maps Meshes Point clouds Patch clouds Volumetric models Layered models The third view can be used for verification Beyond two-view stereo Pick a reference image, and slide the corresponding window along the corresponding epipolar lines of all other images, using inverse depth relative to the first image as the search parameter M. Okutomi and T. Kanade, “A Multiple-Baseline Stereo System,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(4):353-363 (1993). Multiple-baseline stereo Multiple-baseline stereo For larger baselines, must search larger area in second image 1/z width of a pixel width of a pixel 1/z pixel matching score Multiple-baseline stereo Use the sum of SSD scores to rank matches I1 I2 I10 Multiple-baseline stereo results M. Okutomi and T. Kanade, “A Multiple-Baseline Stereo System,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(4):353-363 (1993). Summary: Multiple-baseline stereo Pros Using multiple images reduces the ambiguity of matching Cons Must choose a reference view Occlusions become an issue for large baseline Possible solution: use a virtual view Plane Sweep Stereo Choose a virtual view Sweep family of planes at different depths with respect to the virtual camera each plane defines an image composite homography virtual camera composite input image R. Collins. A space-sweep approach to true multi-image matching. .

• Đồ họa - Thiết kế - Flash
• Computer Vision
• Imagine processing
• Computer science
• Multiple baseline stereo
• Plane sweep stereo
• Volumetric stereo
• Computer vision algorithms
• Computer vision applications
• Image formation
• Feature detection
• Journal of technology and science education
• Experiences using an open source software library
• Teach computer vision subjects
• Computer vision subjects
• Computer vision teaching
• Computer vision difficult
• Aerial perspective
• Local ambiguity
• Introduction to computer vision
• Brief history
• Nearby fields
• Related disciplines
• International Journal of Computer Networks and Communications Security
• Application intelligence and vision in internet of things
• Vision in internet of things
• Wireless sensor and actuators
• Radio Frequency Identification
• Practical Computer
• Vision
• with SimpleCV
• Nathan Oostendorp
• Anthony Oliver
• and Katherine Scott
• Mastering OpenCV
• Vision Projects
• Daniel Lélis Baggio
• Khvedchenia Ievgen
• Scripting Cookbook
• Themes Design
• Mapping with Drupal
• OpenCV 2
• Computer Vision Application
• Programming Cookbook
• Generating Image Descriptions
• Computer Vision Detections
• Margaret Mitchell
• scientific reports
• model language
• process natural language
• Nuclear energy
• Nuclear power plant outage
• Automatically detecting abnormal human
• Field operation and preparation
• Data driven simulation
• The pinhole projection model
• Cameras with lenses
• Digital cameras
• Lesson Capturing light
• Capturing light
• Radiometry of thin lenses
• The physics of light
• Cone sensitivity
• Uniform color spaces
• Lesson Linear filtering
• Linear filtering
• Key properties
• Edge detection
• Origin of edges
• characterizing edges
• Feature extraction
• Corners and blobs
• Finding corners
• Vertical least squares
• Total least squares
• General curves
• The hough transform
• Voting schemes
• Hough transform
• Image alignment
• Bing streetside images
• Single view geometry
• Pinhole camera model
• Principal point
• Two view geometry
• View geometry
• epipolar geometry
• Binocular stereo
• Depth from disparity
• Stereo matching
• Structure from motion
• Projective ambiguity
• Similarity ambiguity
• Object recognition
• History and overview
• Scene categorization