Byeongjoo Ahn

I am a Research Scientist at Apple. I received my Ph.D. degree from Carnegie Mellon University, where I was advised by Prof. Aswin C. Sankaranarayanan and Prof. Ioannis Gkioulekas. Prior to that, I earned my B.S. and M.S. degrees from Seoul National University, where I worked with Prof. Kyoung Mu Lee. Additionally, I spent three wonderful years as a Research Scientist at the Center for Imaging Media Research at the Korea Institute of Science and Technology.

My first name is pronounced as Be-Young-Joo.

Email  /  CV  /  Google Scholar  /  LinkedIn  /  GitHub

profile photo

My research interests are in computational imaging and computer vision. I am interested in identifying visible hints offered by our physical surroundings such as interreflections, and developing imaging systems extending the visibility far beyond human ability such as the reconstruction of objects that are not in the direct line of sight or those with strong self-occlusions.

Novel-View Acoustic Synthesis from 3D Reconstructed Rooms
Byeongjoo Ahn, Karren Yang, Brian Hamilton, Jonathan Sheaffer, Anurag Ranjan, Miguel Sarabia, Oncel Tuzel, Jen-Hao Rick Chang
arXiv, 2023
[code] [paper] [video] (headset recommended)

We unmute novel-view synthesis by estimating the spatial sound anywhere in a scene containing multiple unknown sound sources, by combining blind audio recordings with 3D scene information.

Neural Kaleidoscopic Space Sculpting
Byeongjoo Ahn, Michael De Zeeuw, Ioannis Gkioulekas, Aswin C. Sankaranarayanan
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
[project page] [paper] [supplement] [video] [code] [bibtex]

We introduce a method that recovers full-surround 3D reconstructions from a single kaleidoscopic image by sculpting a neural surface representation.

Kaleidoscopic Structured Light
Byeongjoo Ahn, Ioannis Gkioulekas, Aswin C. Sankaranarayanan
ACM Transactions on Graphics (Proc. SIGGRAPH ASIA), 2021
[project page] [paper] [video] [bibtex]

We introduce a kaleidoscopic structured light system that generates hundreds of virtual projectors and cameras for full surround 3D scans of intricate objects.

Convolutional Approximations to the General Non-Line-of-Sight Imaging Operator
Byeongjoo Ahn, Akshat Dave, Ashok Veeraraghavan, Ioannis Gkioulekas, Aswin C. Sankaranarayanan
IEEE/CVF International Conference on Computer Vision (ICCV), 2019   (Oral Presentation)
[project page] [paper] [supplement] [bibtex]

A computationally efficient technique for NLOS imaging based on a derivation that shows the Gram of the measurement operator is convolutional.

Occlusion-Aware Video Deblurring with a New Layered Blur Model
Byeongjoo Ahn, Tae Hyun Kim, Wonsik Kim, Kyoung Mu Lee
Technical Report, 2016
[paper] [bibtex]

Occlusion-aware deblurring method for scenes with occluding objects using a carefully designed layered blur model that reflects actual blur generation process.

Reduced Illumination Patterns for Acquisition of Specular and Diffuse Normal Maps
Byeongjoo Ahn, Junghyun Cho, Taekyung Yoo, Ig-Jae Kim
ACM SIGGRAPH Asia Posters, 2016
[paper] [bibtex]

Acquisition of specular and diffuse normal maps from minimal number of polarized images by removing the redundancy in four reflectances under XYZ-gradient and constant patterns.

Dynamic Scene Deblurring
Tae Hyun Kim, Byeongjoo Ahn, Kyoung Mu Lee
IEEE International Conference on Computer Vision (ICCV), 2013
[paper] [bibtex]

Dynamic scene deblurring method estimating the latent image as well as different blur motions and their soft segmentations jointly.

  • Program Committee, ICCP 2023
  • Volunteer, Camera Building Workshop as part of Gelfand Outreach Program at CMU (2019)
  • Student Volunteer, ACCV 2012, ICCP 2021
Graduate Coursework
  • 16-889 Learning for 3D Vision  -  Spring 2022
  • 15-868 Physics-based Rendering  -  Spring 2021
  • 33-353 Intermediate Optics  -  Fall 2020
  • 15-858 Discrete Differential Geometry  -  Spring 2020
  • 18-771 Linear Systems  -  Fall 2019
  • 10-707 Deep Learning  -  Spring 2019
  • 10-725 Convex Optimization  -  Fall 2018
  • 16-823 Physics based Methods in Vision  -  Spring 2018
  • 10-701 Introduction to Machine Learning  -  Spring 2018
  • 16-720B Computer Vision  -  Fall 2017
  • 18-793 Image and Video Processing  -  Fall 2017
  • 36-705 Intermediate Statistics  -  Fall 2017

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