What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Augmented reality
David Kim mainly investigates Computer vision, Artificial intelligence, Computer graphics, Augmented reality and Pipeline.
His work in the fields of Tracking and Motion overlaps with other areas such as Spatial interaction, Kinematics and Wrist.
His work is dedicated to discovering how Tracking, Object are connected with 3D reconstruction, Segmentation and Surface reconstruction and other disciplines.
Many of his research projects under Artificial intelligence are closely connected to Electronics with Electronics, tying the diverse disciplines of science together.
His work on Graphics hardware as part of general Computer graphics research is often related to Position, Simultaneous localization and mapping and Current sensor, thus linking different fields of science.
As a member of one scientific family, David Kim mostly works in the field of Augmented reality, focusing on Mobile device and, on occasion, Distributed computing and Embedded system.
His most cited work include:
- KinectFusion: Real-time dense surface mapping and tracking (2794 citations)
- KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera (1650 citations)
- Digits: freehand 3D interactions anywhere using a wrist-worn gloveless sensor (358 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Computer graphics, Object and Human–computer interaction.
His work on Augmented reality, Image, Tracking and Silhouette as part of general Computer vision research is frequently linked to Pipeline, thereby connecting diverse disciplines of science.
His work deals with themes such as 3D reconstruction, Perspective, Transparent display and Rendering, which intersect with Augmented reality.
His work in the fields of Computer graphics, such as Graphics, intersects with other areas such as Position, Display device and Volume.
His research in Object intersects with topics in Segmentation, Computer graphics, Engineering drawing and Gesture recognition.
His biological study spans a wide range of topics, including Multimedia, Mobile device and Gesture.
He most often published in these fields:
- Artificial intelligence (66.67%)
- Computer vision (64.71%)
- Computer graphics (31.37%)
What were the highlights of his more recent work (between 2014-2020)?
- Artificial intelligence (66.67%)
- Computer vision (64.71%)
- Object (25.49%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Object, Pixel and Pattern recognition are his primary areas of study.
His work on Feature and Augmented reality as part of general Artificial intelligence study is frequently linked to Interior design, bridging the gap between disciplines.
His work in the fields of Computer vision, such as Tracking, 3d sensing and Segmentation, overlaps with other areas such as Pipeline and Ultrashort pulse.
David Kim interconnects Robot, Session, Computer graphics and Engineering drawing in the investigation of issues within Object.
His research investigates the connection between Pixel and topics such as Structured light that intersect with issues in Correspondence problem, Random forest, Robustness and Projector.
The concepts of his Pattern recognition study are interwoven with issues in Data point and Silhouette.
Between 2014 and 2020, his most popular works were:
- Accurate, Robust, and Flexible Real-time Hand Tracking (283 citations)
- Fusion4D: real-time performance capture of challenging scenes (266 citations)
- Holoportation: Virtual 3D Teleportation in Real-time (245 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Augmented reality
David Kim focuses on Computer vision, Artificial intelligence, Pipeline, Motion capture and Orders of magnitude.
His work on Discriminative model expands to the thematically related Computer vision.
To a larger extent, David Kim studies Motion with the aim of understanding Motion capture.
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