BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Australia/Melbourne
X-LIC-LOCATION:Australia/Melbourne
BEGIN:DAYLIGHT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
TZNAME:AEDT
DTSTART:19721003T020000
RRULE:FREQ=YEARLY;BYMONTH=4;BYDAY=1SU
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:19721003T020000
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
TZNAME:AEST
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260114T163631Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_868@linklings.com
SUMMARY:Thin On-Sensor Nanophotonic Array Cameras
DESCRIPTION:Praneeth Chakravarthula (Princeton University); Jipeng Sun (Pr
 inceton University, Northwestern University); Xiao Li, Chenyang Lei, Gene 
 Chou, and Mario Bijelic (Princeton University); Johannes Froesch and Arka 
 Majumdar (University of Washington); and Felix Heide (Princeton University
 )\n\nToday's commodity camera systems rely on compound optical systems to 
 map light originating from the scene to positions on the sensor where it g
 ets recorded as an image. To achieve an accurate mapping without optical a
 berrations, i.e., deviations from Gauss' linear optics model, typical lens
  systems introduce increasingly complex stacks of optical elements respons
 ible for the height of existing commodity cameras. In this work, we invest
 igate flat nanophotonic computational cameras as an alternative that emplo
 ys an array of skewed lenslets and a learned reconstruction method. The op
 tical array is embedded in a metasurface that, at 700nm height, is flat an
 d sits on the sensor cover glass at a 2mm focal distance from the sensor. 
 To tackle the highly chromatic response of a metasurface and design an arr
 ay over the entire sensor, we propose a differentiable optimization method
  that samples continuously over the spectrum and that factorizes the optic
 al modulation for different optical fields into individual lenses of an ar
 ray. We reconstruct a megapixel image from our flat imager with a learned 
 probabilistic reconstruction method that employs a generative diffusion mo
 del to sample an implicit prior. To tackle scene-dependent aberrations in 
 broadband, we propose a method for acquiring paired real-world training da
 ta in diverse illumination conditions. We assess the proposed flat camera 
 design in simulation and with an experimental prototype, validating that t
 he method is capable of recovering high-quality images outside the lab in 
 broadband with a single flat metasurface optic.\n\nRegistration Category: 
 Full Access, Enhanced Access, Trade Exhibitor, Experience Hall Exhibitor\n
 \n
URL:https://asia.siggraph.org/2023/full-program?id=papers_868&sess=sess209
END:VEVENT
END:VCALENDAR