What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Ruigang Yang spends much of his time researching Artificial intelligence, Computer vision, Pixel, Stereopsis and Computer graphics.
His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence.
Ruigang Yang has included themes like Computer graphics, Benchmark and Pattern recognition in his Computer vision study.
Ruigang Yang interconnects Depth map, Digital image, Image fusion and Image warping in the investigation of issues within Pixel.
His research investigates the link between Stereopsis and topics such as Weighting that cross with problems in Vector processor and Dynamic programming.
The various areas that he examines in his Computer graphics study include Face model, Projector, Videoconferencing and Video image.
His most cited work include:
- Spatial-Depth Super Resolution for Range Images (669 citations)
- Detailed Real-Time Urban 3D Reconstruction from Video (650 citations)
- Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling (482 citations)
What are the main themes of his work throughout his whole career to date?
Ruigang Yang mostly deals with Artificial intelligence, Computer vision, Computer graphics, Point cloud and Pixel.
Ruigang Yang works mostly in the field of Artificial intelligence, limiting it down to topics relating to Pattern recognition and, in certain cases, Face, as a part of the same area of interest.
His research integrates issues of Frame and Computer graphics in his study of Computer vision.
His Computer graphics research is multidisciplinary, incorporating elements of Projector and Videoconferencing.
His Point cloud research includes themes of Matching, Lidar and Object detection.
His study in Pixel is interdisciplinary in nature, drawing from both Algorithm and Convolutional neural network.
He most often published in these fields:
- Artificial intelligence (71.73%)
- Computer vision (61.01%)
- Computer graphics (16.37%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (71.73%)
- Computer vision (61.01%)
- Point cloud (13.99%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Point cloud, Position and Obstacle are his primary areas of study.
His research on Artificial intelligence often connects related areas such as Pattern recognition.
His Pattern recognition research is multidisciplinary, relying on both Artificial neural network and Face.
Ruigang Yang combines subjects such as Discriminative model and Feature learning with his study of Computer vision.
His Point cloud research incorporates elements of Frame, Lidar, Ground truth and Object detection.
His Pixel study combines topics from a wide range of disciplines, such as Depth map, Algorithm, Convolutional neural network and Kernel.
Between 2018 and 2021, his most popular works were:
- GA-Net: Guided Aggregation Net for End-To-End Stereo Matching (157 citations)
- TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents (141 citations)
- Salient Object Detection in the Deep Learning Era: An In-Depth Survey (120 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of investigation include Artificial intelligence, Computer vision, Point cloud, Object detection and Algorithm.
His biological study spans a wide range of topics, including Machine learning and Pattern recognition.
His Computer vision research is multidisciplinary, incorporating elements of Robot, Discriminative model and Flexibility.
His Discriminative model course of study focuses on Image and Pixel.
His Point cloud research integrates issues from Frame, Lidar and Pose.
His Algorithm study integrates concerns from other disciplines, such as End-to-end principle, Block and Matching.
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