Devarth Parikh
Papers
1
Total Citations
7
H-Index
1
About
Devarth Parikh is a researcher at the forefront of sensor fusion and depth estimation for autonomous systems. His work bridges the gap between high-cost, low-resolution active sensors like LiDAR and affordable, high-resolution passive imaging, a critical challenge for self-driving vehicles, robotics, and augmented reality. In his highly cited 2021 paper, "Extending Single Beam LiDAR To Full Resolution By Fusing with Single Image Depth Estimation," Parikh pioneered a method to dramatically upscale sparse single-beam LiDAR data by fusing it with monocular depth predictions from a single image. This approach offers a practical, cost-effective path to achieving dense, high-fidelity depth maps without expensive multi-beam sensors. While his citation count is early-stage, the core idea—leveraging deep learning to compensate for hardware limitations—positions his work as a foundational contribution to efficient perception systems. Parikh’s research directly addresses the trade-off between sensor cost and resolution, making high-quality depth sensing more accessible for real-world deployment in autonomous navigation and scene understanding.
Research Focus
Key Achievements
Top Papers
- 1