1. Computational Rephotography Soonmin Bae Aseem Agarwala Frédo Durand

2. What is Rephotography The act of repeat photography of the same site – wikipedia Visualize changes over time Changes of a city or buildings Changes of a city or buildings Effects of erosion Effects of erosion Need to decide viewpoint viewpoint season and time of day season and time of day cameras and lenses cameras and lenses

3. Newyork changing by Douglas Levere (New York City photographer) Roadway to the Battery, Manhattan, 1938 and 1997 Custon house statues and New york produce exchange, Manhattan, 1936 Native American museum statues and MTA headquarters, Manhattan, 1997

4. Urban Life though Two Lenses - 1890 and 2002

5. Goals Make rephotography easy lead photographers to choosing a desired viewpoint matching that of a given image lead photographers to choosing a desired viewpoint matching that of a given image Visualize the difference between a given image and the current scene show how to translate and rotate the camera show how to translate and rotate the camera Interact with users in a real time Interact with users in a real time

6. Related work Depth (or shape) from stereo estimate a 3D structure from two images using triangulation estimate a 3D structure from two images using triangulation find pairs of corresponding image points and measure the difference to infer its depth find pairs of corresponding image points and measure the difference to infer its depth Panoramic mosaic increase FOV increase FOV use image-warping with homography (reprojection) use image-warping with homography (reprojection) Visual homing lead a robot to a desired positions and orientations lead a robot to a desired positions and orientations Recover the epipolar geometry relating the current image taken by the robot and the target image Recover the epipolar geometry relating the current image taken by the robot and the target image

7. Background Homography defines a geometric relation between two images defines a geometric relation between two images Epipolar geometry find the geometric relation (homography) between two images of a single 3D scene find the geometric relation (homography) between two images of a single 3D scene SIFT (scale-invariant feature transform) [Lowe 04] extract distinctive features from images extract distinctive features from images the features are invariant to image scale, rotation, and partially invariant to changing viewpoints, change in illumination the features are invariant to image scale, rotation, and partially invariant to changing viewpoints, change in illumination SURF (speeded up robust features) SURF (speeded up robust features)

8. Our Approach find and match distinctive features of a given image and the scene using Computer vision techniques (e.g., SIFT and SURF) using Computer vision techniques (e.g., SIFT and SURF) compute the amount of translation using epipolar geometry using epipolar geometry interact with users in a real time and lead to a desired viewpoint using visualization using visualization resolve rotation using image warping with homographies using image warping with homographies or lead users to rotating the camera or lead users to rotating the camera