What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of investigation include Artificial intelligence, Computer vision, Object, Information retrieval and RGB color model.
As part of one scientific family, Manolis Savva deals mainly with the area of Artificial intelligence, narrowing it down to issues related to the Machine learning, and often Procedural modeling.
Computer vision is closely attributed to Artificial neural network in his study.
The various areas that Manolis Savva examines in his Object study include Computer graphics, Data visualization and Benchmark.
His Information retrieval study integrates concerns from other disciplines, such as Deep learning and Taxonomy.
His Voxel research incorporates elements of Depth map, Leverage and Viewing frustum.
His most cited work include:
- ShapeNet: An Information-Rich 3D Model Repository (1726 citations)
- ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (756 citations)
- Semantic Scene Completion from a Single Depth Image (669 citations)
What are the main themes of his work throughout his whole career to date?
Manolis Savva mainly investigates Artificial intelligence, Object, Computer vision, RGB color model and Human–computer interaction.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition.
His study in Object is interdisciplinary in nature, drawing from both Representation and Deep learning.
His Deep learning research includes themes of Artificial neural network and Information retrieval.
His research investigates the connection between Computer vision and topics such as Computer graphics that intersect with issues in Taxonomy, Data visualization and WordNet.
Manolis Savva works mostly in the field of RGB color model, limiting it down to topics relating to Leverage and, in certain cases, Depth map, Viewing frustum and Voxel, as a part of the same area of interest.
He most often published in these fields:
- Artificial intelligence (65.71%)
- Object (24.29%)
- Computer vision (22.86%)
What were the highlights of his more recent work (between 2019-2021)?
- Human–computer interaction (15.71%)
- Artificial intelligence (65.71%)
- Reinforcement learning (12.86%)
In recent papers he was focusing on the following fields of study:
Manolis Savva focuses on Human–computer interaction, Artificial intelligence, Reinforcement learning, Task and Object.
Manolis Savva interconnects Annotation, Motion and Solid modeling in the investigation of issues within Human–computer interaction.
Manolis Savva studies Artificial intelligence, focusing on Relational graph in particular.
His Reinforcement learning research is multidisciplinary, incorporating elements of RGB color model and Computer engineering.
His RGB color model research focuses on subjects like Distributed computing, which are linked to Leverage.
His studies in Computer engineering integrate themes in fields like Frame rate and Task.
Between 2019 and 2021, his most popular works were:
- DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames (57 citations)
- ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects. (17 citations)
- Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Embodied cognition, Task, Human–computer interaction, Object and Software deployment.
Embodied cognition is connected with Group, State and Focus in his study.
His Software deployment study spans across into subjects like Machine learning, Artificial intelligence, Bridge, Code and Space.
His research on Machine learning often connects related topics like Robot.
Manolis Savva has included themes like RGB color model and Reinforcement learning in his Robot study.
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