Marcelo Saval-Calvo
Papers
8
Total Citations
147
H-Index
7
About
Marcelo Saval-Calvo is a computer vision and robotics researcher whose work centers on 3D sensing, RGB-D sensor technology, and autonomous systems perception. He has made significant contributions to the development and evaluation of methodologies for depth-sensing cameras, establishing himself as an authority on sensor calibration and data registration techniques. His highly cited 2017 study on RGB-D sensor calibration (34 citations) and his 2014 comparative analysis of registration methods for RGB-D video (32 citations) have become valuable reference works for researchers building 3D reconstruction and robotic mapping systems. His 2017 review of the Bayesian Occupancy Filter (32 citations) demonstrates a broader engagement with autonomous vehicle perception, a field of rapidly growing scientific and industrial importance. Beyond comparative studies, Saval-Calvo has contributed practical tools and datasets, including a multi-sensor 3D object dataset designed to support object recognition research, and the μ-MAR multiplane marker-based registration system. His exploration of dynamic next-best viewpoint tracking for robot-mounted cameras further reflects his commitment to bridging theoretical computer vision with real-world robotic applications, making his body of work particularly relevant to students working at the intersection of robotics, 3D vision, and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Comparative Study of Registration Methods for RGB-D Video of Static Scenes32 citations · 2014
- 3A Review of the Bayesian Occupancy Filter32 citations · 2017
- 4
- 5μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras16 citations · 2015
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- 8