Papers
11
Total Citations
134
H-Index
6
About
Paolo Russo’s research lies at the intersection of computer vision, robotics, and embedded systems, with a primary focus on monocular depth estimation and attitude estimation for autonomous agents. His most cited work, “(DE)²CO: Deep Depth Colorization” (37 citations), addresses the fundamental robotic need for depth information beyond RGB data, while his early collaboration with NIST on 3D range cameras for healthcare mobility aids (36 citations) demonstrates the applied impact of his research. Russo has made significant contributions to making depth estimation practical for resource-constrained environments, developing lightweight models like SPEED (16 citations) that enable real-time monocular depth estimation on low-power IoT and embedded devices. His DANAE series (cumulatively over 20 citations) tackles the challenging problem of denoising attitude estimation for underwater robots, where sensor noise and irregular water conditions complicate accurate positioning. More recently, Russo has explored optimizing Vision Transformer architectures for monocular depth estimation and energy-aware models for both terrestrial and underwater scenarios. His work consistently bridges the gap between state-of-the-art deep learning techniques and the practical constraints of real-world robotic systems, from healthcare to maritime navigation.
Research Focus
Key Achievements
Top Papers
- 1(DE)$^2$CO: Deep Depth Colorization37 citations · 2018
- 2Applications of a 3D Range Camera Towards Healthcare Mobility Aids36 citations · 2006
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- 4DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation10 citations · 2021
- 5
- 6DOES: A Deep Learning-Based Approach to Estimate Roll and Pitch at Sea8 citations · 2022
- 7DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation6 citations · 2021
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- 9DANAE: a denoising autoencoder for underwater attitude estimation4 citations · 2020
- 10