Nibarkavi Amutha
Papers
1
Total Citations
2
H-Index
1
About
Nibarkavi Amutha is a rising researcher at the forefront of novel view synthesis for underwater perception, with a primary focus on advancing imaging sonar technology. Her most notable contribution, the paper "SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting" (2025), introduces a groundbreaking framework that adapts Gaussian splatting—a technique popular in computer vision for optical scenes—to the unique challenges of acoustic imaging. This work models the scene as a set of 3D Gaussians with acoustic reflectance and saturation properties, enabling realistic novel view synthesis while accurately simulating acoustic streaking phenomena, a persistent challenge in sonar data. Though early in its trajectory, the paper has already garnered 2 citations, signaling its potential to reshape underwater robotics and autonomous navigation by improving how machines interpret sonar imagery. Amutha’s research bridges computer graphics and marine technology, offering practical solutions for mapping and exploration in low-visibility environments. Her innovative approach positions her as a key contributor to the growing field of neural rendering for non-optical sensors, with implications for both academic research and real-world applications in oceanography and defense.
Research Focus
Key Achievements
Top Papers
- 1SonarSplat: Novel View Synthesis of Imaging Sonar via Gaussian Splatting2 citations · 2025