Shubhajit Basak
Papers
1
Total Citations
6
H-Index
1
About
Shubhajit Basak is a researcher whose work centers on computer vision, with a particular focus on monocular depth estimation—a fundamentally challenging problem in fields like robotic perception, augmented reality, and 3D reconstruction. His major contribution lies in systematically analyzing the building blocks that make depth estimation models successful. In his highly cited 2021 review, Basak provides a comprehensive survey of benchmark datasets and training loss functions, offering a critical roadmap for practitioners navigating this ill-posed problem. This work has garnered 6 citations, establishing him as a thoughtful synthesizer in a rapidly evolving domain. By demystifying how dataset diversity and loss design impact model performance, Basak’s research helps bridge the gap between theoretical advances and practical deployment. His review serves as an essential reference for students and engineers alike, making complex trade-offs accessible and guiding future innovation in depth-from-image techniques.
Research Focus
Key Achievements
Top Papers
- 1