Pratik Parihar
Papers
1
Total Citations
3
H-Index
1
About
Pratik Parihar is a computer vision researcher whose work focuses on advancing real-time 3D perception for autonomous systems. His primary research areas include 3D object detection, bounding box estimation, and adaptive projection techniques for dynamic environments. Parihar’s most notable contribution is his work on real-time 3D bounding box estimation using RCNN-Resnet101 architectures combined with adaptive projection matrices, a method that significantly improves the accuracy and speed of spatial object localization in applications ranging from autonomous driving to robotics and augmented reality. This approach addresses critical challenges in real-time performance and geometric precision, making it highly relevant for practical deployment. With his 2024 paper already garnering citations, Parihar’s work is gaining traction in the computer vision community. His research bridges the gap between deep learning efficiency and geometric robustness, offering scalable solutions for next-generation perception systems. As the demand for reliable 3D understanding in autonomous navigation and interactive environments grows, Parihar’s contributions position him as an emerging voice in real-time computer vision, with potential for lasting impact on both academic research and industry applications.
Research Focus
Key Achievements
Top Papers
- 1