Junjie Luo

PhD Student, Electrical and Computer Engineering

Purdue University

About

I am a PhD student in the School of Electrical and Computer Engineering at Purdue University, where I work on passive depth sensing and computational imaging. My research focuses on depth-from-defocus (DfD) methods, with an emphasis on Depth from Coupled Optical Differentiation (COD) and real-time depth estimation.

Broadly, I am interested in designing imaging systems and algorithms that extract reliable 3D information from minimal measurements, especially under photon-limited or hardware-constrained settings.

Research Interests

  • Depth from Defocus / Differential Defocus
  • Computational imaging and 3D computer vision
  • Real-time depth estimation and system prototyping
  • Optical differentiation and defocus-based sensing

Education

  • PhD, Electrical and Computer Engineering
    Purdue University, West Lafayette, IN, USA
    In progress
  • MS, Computer Information and Technology
    Purdue University, West Lafayette, IN, USA
  • BS, Computer Science
    Sun Yat-sen University, Guangzhou, China

Publications

Focal Split: Untethered Snapshot Depth from Differential Defocus

Luo, J., Mamish, J., Fu, A., Concannon, T., Hester, J., Alexander, E., Guo, Q.

Abstract. We present Focal Split, a snapshot depth-from-differential-defocus method that uses two images captured with different sensor distances via a beamsplitter to recover dense, long-range depth with a compact form factor.

Link: [paper]  |  Project page: [page]

Focal Split project logo
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.

Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries

Xu, W., Wagner, C., Luo, J., Guo, Q.

Abstract. We propose a boundary-focused approach for depth estimation under extreme photon limitations, leveraging defocused edges to obtain robust depth measurements when conventional methods fail.

Link: [paper]  |  Project page: [page]

Blurry-Edges project logo
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.

Depth from Coupled Optical Differentiation

Luo, J., Liu, Y., Alexander, E., Guo, Q.

Abstract. We propose depth from coupled optical differentiation, a low-computation passive-lighting 3D sensing mechanism that exploits jointly designed optical transfer functions and computational reconstruction.

Link: [paper]  |  Project page: [page]

Depth from Coupled Optical Differentiation project logo
International Journal of Computer Vision (IJCV), 2025.

CT-Bound: Fast Boundary Estimation From Noisy Images

Xu, W., Luo, J., Guo, Q.

Abstract. We introduce CT-Bound, a robust and computationally efficient boundary detection method designed for very noisy image regimes, with applications to photon-limited imaging.

Link: [paper]

CT-Bound project logo
IEEE 26th International Workshop on Multimedia Signal Processing (MMSP), 2024.

Generative Quanta Color Imaging

Purohit, V., Luo, J., Chi, Y., Guo, Q., Chan, S. H., Qiu, Q.

Abstract. We explore generative modeling for color image formation with single-photon cameras, enabling high-quality reconstructions from severely photon-limited measurements.

Link: [paper]

Generative Quanta Color Imaging project logo
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

Contact

For research inquiries, collaborations, or questions about my work, feel free to contact me:

  • Email: luo330@purdue.edu
  • Office: Flex Lab 3095, 205 Gates Rd, West Lafayette, IN 47906, USA