PPT-Photometric stereo Radiance

Photometric stereo Radiance Pixels measure radiance This pixel Measures radiance along this ray Where do the rays come from Rays from the light source reflect off a surface and reach camera Reflection Surface absorbs light energy and radiates it back. 01 THDN 700W 1200W 1400W 2400W 2800W 20Hz20kHz 01 THDN 650W 1100W 1100W 2200W 2200W Figures are watts per channel both channels driven Product Description The Crest Audio CA12 professional power amplifier is designed to achieve unsurpassed sonic perf Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. image 1. image 2. Dense depth map. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. : . Uncalibrated Photometric Stereo . with . Shadows. Kalyan Sunkavalli, . Harvard University. Joint work with Todd Zickler and Hanspeter Pfister. Published in the Proceedings of ECCV 2010. http://gvi.seas.harvard.edu/. Outline. 1.) Step 1 – TV3 Calibration. 2.) Step 2 – SMOV4 (+ Cycle 17 Calibration). 3.) Step 3 – Final Calibration (present analysis). - More data. - New flat fields. - Astrodrizzle. - Precise calibration. A. . Pasewaldt. 1. , . K. . Willner. 2. , . J. . Oberst. 1,2. , . Frank . Scholten. 1. , . M. . Wählisch. 1. , . K.-D. . Matz. 1. , . T. . Roatsch. 1. , . H. . Hoffmann. 1. , . A. . Grechishev. 3. . RESEARCH. q. uality and quantity. Paul Dowling. Institute of Education. University of London. Photometric . redshifts. can be routinely obtained to accuracies of better than 0.1 in Deltaz/(1 +. z. ). The issue of dust extinction, however, is one that has still not been well quantified. In this paper the success of two template-fitting photometric . Many slides adapted from Steve Seitz. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. image 1. image 2. Dense depth map. Binocular stereo. Given a calibrated binocular stereo pair, fuse it to produce a depth image. Many slides adapted from S. Seitz. Multi-view . stereo. Generic problem formulation: given several images of the same object or scene, compute a representation of its 3D shape. Reconstruction (side). If necessary, rectify the two stereo images to transform . epipolar. lines into . scanlines. For each pixel x in the first image. Find corresponding . epipolar. . scanline. in the right image. Examine all pixels on the . Many slides drawn from Lana . Lazebnik. , UIUC. Basic stereo matching algorithm. For each pixel in the first image. Find corresponding . epipolar. line in the right image. Examine all pixels on the . 6.819 / 6.869: Advances in Computer Vision. Antonio Torralba. Camera Models. ?. ?. Perspective projection. Virtual image plane. (0,0,0). Perspective projection. (0,0,0). f. Z. Y. y. ?. y. = . f. Y/Z. 1. 2. Why do we perceive depth?. 3. What do humans use as depth cues?. Convergence . When watching an object close to us, our eyes point slightly inward. This difference in the direction of the eyes is called convergence. This depth cue is effective only on short distances (less than 10 meters). . Announcements. Project 4 (stereo) released today, due Friday, April 1, at 8pm. To be done in groups of 2. Recap: . Lambertian (Diffuse) Reflectance. I . : observed image intensity. k. d. . : object albedo. Several slides from Larry . Zitnick. and Steve Seitz. Why do we perceive depth?. What do humans use as depth cues?. Convergence . When watching an object close to us, our eyes point slightly inward. This difference in the direction of the eyes is called convergence. This depth cue is effective only on short distances (less than 10 meters). .