Papers
3
Total Citations
22
H-Index
3
About
Sai Manoj Prakhya is a researcher specializing in 3D computer vision, robotic perception, and spatial mapping systems, with contributions spanning LiDAR-based mapping, feature descriptors, and visual localization. His most notable work introduces a lifelong 3D mapping framework — a modular, cloud-native system capable of supporting both hand-held and robot-mounted LiDAR platforms, incorporating dynamic point removal, multi-session alignment, and map change detection. This contribution, already accumulating 10 citations since its 2024 publication, addresses a critical challenge in long-term autonomous navigation and mapping at scale. Prakhya has also made meaningful strides in efficient 3D feature representation; his 2017 work on creating low-dimensional 3D feature descriptors using PCA responds directly to the computational constraints of mobile and embedded depth-sensing applications, earning 9 citations and remaining relevant to practitioners building resource-limited robotic systems. His 2023 research on implicit scene geometry learning for global visual localization reflects his sustained interest in bridging deep learning with real-world pose estimation for robotics and augmented reality. Across his work, Prakhya consistently targets practical deployment challenges, making his research particularly valuable to engineers and researchers building real-world autonomous and mixed-reality systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2On creating low dimensional 3D feature descriptors with PCA9 citations · 2017
- 3Implicit Learning of Scene Geometry From Poses for Global Localization3 citations · 2023