Gaurav Bansal
Papers
1
Total Citations
20
H-Index
1
About
Gaurav Bansal is a researcher specializing in computer vision and deep learning, with a particular focus on self-supervised depth estimation from monocular videos. His most notable contribution is the development of $$S^3$$Net (Semantic-Aware Self-supervised Depth Estimation), a pioneering framework that integrates semantic understanding into depth prediction tasks. This work, published in 2020, has garnered 20 citations, reflecting its impact on advancing self-supervised learning methods that leverage both real-world monocular videos and synthetic data. Bansal’s approach addresses a critical challenge in autonomous systems—enabling accurate depth perception without costly labeled datasets—by incorporating semantic cues to improve robustness in complex scenes. His research bridges the gap between synthetic and real-world domains, offering practical solutions for applications like robotics and autonomous driving. Bansal’s work is recognized for its innovative fusion of semantic awareness with self-supervised learning, setting a foundation for more efficient and scalable depth estimation techniques. His contributions continue to inspire further exploration in unsupervised geometric understanding and domain adaptation.
Research Focus
Key Achievements
Top Papers
- 1