What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Point cloud, Computer vision, Deep learning and Feature extraction are his primary areas of study.
The Inertial measurement unit research Gim Hee Lee does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Encoder, therefore creating a link between diverse domains of science.
He works mostly in the field of Point cloud, limiting it down to topics relating to Leverage and, in certain cases, Pattern recognition, Inference and Discriminative model.
His study in the field of Pose and Depth map also crosses realms of Pipeline transport, Obstacle and Visual perception.
His work carried out in the field of Pose brings together such families of science as Stereopsis and Stereo cameras.
Gim Hee Lee has included themes like Segmentation, Feature vector and k-nearest neighbors algorithm in his Deep learning study.
His most cited work include:
- SO-Net: Self-Organizing Network for Point Cloud Analysis (271 citations)
- PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision (268 citations)
- Vision-based autonomous mapping and exploration using a quadrotor MAV (242 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Point cloud, Pose and Algorithm.
As part of his studies on Artificial intelligence, Gim Hee Lee often connects relevant subjects like Pattern recognition.
When carried out as part of a general Computer vision research project, his work on Stereo camera, RANSAC and Inertial measurement unit is frequently linked to work in Matching, therefore connecting diverse disciplines of study.
The various areas that Gim Hee Lee examines in his Point cloud study include Segmentation, Data mining, Feature vector, Ground truth and Feature extraction.
His research integrates issues of Optical flow and Robustness in his study of Pose.
His study in Algorithm is interdisciplinary in nature, drawing from both Convolution and Convolutional neural network.
He most often published in these fields:
- Artificial intelligence (80.36%)
- Computer vision (44.64%)
- Point cloud (30.36%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (80.36%)
- Point cloud (30.36%)
- Algorithm (24.11%)
In recent papers he was focusing on the following fields of study:
Gim Hee Lee mainly investigates Artificial intelligence, Point cloud, Algorithm, Pose and Pattern recognition.
His Artificial intelligence course of study focuses on Computer vision and Markov chain.
The Point cloud study combines topics in areas such as Segmentation, Feature, Rigid transformation, Deep learning and Ground truth.
His Deep learning research includes themes of Tree traversal, Change detection and Thresholding.
His Ground truth study combines topics in areas such as Kullback–Leibler divergence, Point distribution model and Data mining.
His Algorithm study combines topics from a wide range of disciplines, such as Convolution, Kernel and Robustness.
Between 2019 and 2021, his most popular works were:
- RPM-Net: Robust Point Matching Using Learned Features (32 citations)
- Cascaded Refinement Network for Point Cloud Completion (24 citations)
- Weakly Supervised Semantic Point Cloud Segmentation: Towards 10× Fewer Labels (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Point cloud, Computer vision, Pattern recognition and Pose.
Artificial intelligence is frequently linked to Algorithm in his study.
As a part of the same scientific study, Gim Hee Lee usually deals with the Algorithm, concentrating on Iterative closest point and frequently concerns with Robustness.
Gim Hee Lee undertakes multidisciplinary studies into Computer vision and Network architecture in his work.
The study incorporates disciplines such as Categorical variable and Joint in addition to Pattern recognition.
His Pose research is multidisciplinary, relying on both Epipolar geometry and Correspondence problem.
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