Sreenithy Chandran

Papers

Total Citations

H-Index

About

Sreenithy Chandran is a researcher specializing in computational imaging, non-line-of-sight (NLOS) sensing, and active illumination systems — areas with significant implications for robotics, autonomous vehicles, and surveillance technologies. Chandran's most notable contribution, "Adaptive Lighting for Data-Driven Non-Line-of-Sight 3D Localization and Object Identification" (2019), addresses one of computer vision's most formidable challenges: reconstructing and identifying objects hidden from both camera and light source. By leveraging data-driven approaches with adaptive lighting strategies, this work helped advance NLOS imaging beyond the constraints of expensive time-resolved measurement hardware, making the field more accessible and practical. Garnering 10 citations, it represents a meaningful step forward in a rapidly evolving domain. Chandran's subsequent work on "Slope Disparity Gating" (2022) further demonstrates a sustained commitment to innovative active illumination techniques, introducing methods for selectively imaging photons reflected from specific surface geometries at a distance. Together, these contributions reflect a coherent and forward-looking research vision focused on seeing beyond physical limitations — a pursuit with profound real-world applications in safety-critical and intelligent systems.