Liang Chang

Guilin University of Electronic Technology

Papers

Total Citations

H-Index

About

Liang Chang is a computer vision researcher whose work centers on 3D reconstruction, depth estimation, and intelligent visual systems. His most recognized contribution, "Real-Time 3D Reconstruction Method Based on Monocular Vision" (2021), addresses fundamental challenges in recovering three-dimensional spatial information from single-camera inputs — a technically demanding problem with far-reaching implications. This work, which has garnered 12 citations, tackles core limitations in accuracy, computational efficiency, and scalability that have historically constrained real-time 3D reconstruction systems. By advancing monocular vision-based approaches, Chang's research directly enables progress in high-impact application domains including virtual reality, industrial automation, and autonomous mobile robot navigation. His focus on real-time performance is particularly significant, as bridging the gap between theoretical reconstruction methods and deployable, time-sensitive systems remains one of the field's persistent challenges. Though early in citation accumulation, his work speaks to a growing demand for lightweight yet robust vision pipelines that do not rely on expensive multi-camera setups. Students and researchers working at the intersection of robotics, augmented reality, or autonomous systems will find Chang's methodological contributions a valuable reference point for understanding modern monocular depth and scene reconstruction techniques.