Papers

14

Total Citations

366

H-Index

10

About

Elisa Ricci is a prominent computer vision and robotics researcher whose work spans depth estimation, semantic place recognition, domain adaptation, and robotic perception. She has made significant contributions to the integration of deep learning with structured probabilistic models, most notably through her highly cited work on monocular depth estimation, which employs multi-scale convolutional neural networks combined with continuous Conditional Random Fields to recover 3D scene structure from single images — a paper that has garnered over 100 citations. Her research consistently bridges visual perception and robotic applications, as demonstrated through her investigations into RGB-D representation learning, visual odometry evaluation, and LiDAR semantic segmentation across diverse domains. Ricci has also advanced the field of transfer learning, developing methods for domain adaptation in semantic place recognition and personalized gesture-based human-robot interaction with UAVs. Her work on open-world recognition tackles the fundamental challenge of enabling robots to identify previously unseen objects by leveraging web-sourced knowledge. Collectively, her portfolio reflects a sustained commitment to making robotic systems more perceptually robust and adaptable in real-world, unconstrained environments — a body of work that has accumulated hundreds of citations and meaningfully shaped modern robotic vision research.

Research Focus

Key Achievements

10
H-Index
14
Papers
366
Total Citations
26
Avg Citations/Paper
🏆 Most Cited Paper
Monocular Depth Estimation Using Multi-Scale Continuous CRFs as Sequential Deep Networks
103 citations · 2018
📈 Most Prolific Year: 2014 (4 Papers)
🤝 Key Collaborators: 61
🏛 Institutions: University of Trento, Fondazione Bruno Kessler, University of Perugia

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago