Yash Bhalgat
Papers
1
Total Citations
24
H-Index
1
About
Yash Bhalgat is a researcher at the forefront of 3D computer vision and robotics, with a focus on large-scale scene reconstruction and neural representation learning. His most cited work, "SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection" (2024, 24 citations), introduces a pioneering system that fuses lidar and visual data to produce geometrically accurate, photo-realistic 3D reconstructions at scale. By adapting neural radiance fields (NeRFs) for robotic inspection, Bhalgat addresses a critical challenge in autonomous systems: generating high-fidelity models of real-world environments that are both precise and visually rich. This contribution bridges the gap between traditional geometric mapping and modern neural rendering, enabling robots to perceive and navigate complex spaces with unprecedented detail. His work has immediate applications in infrastructure monitoring, autonomous navigation, and digital twin creation. With a growing citation impact, Bhalgat is recognized for pushing the boundaries of how machines understand and reconstruct the physical world, making his research essential reading for students and engineers working in embodied AI, 3D vision, and field robotics.
Research Focus
Key Achievements
Top Papers
- 1