What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Geometry
Baoquan Chen mainly investigates Artificial intelligence, Computer vision, Point cloud, Computer graphics and Rendering.
In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition.
His work on Level set method and Image as part of general Computer vision research is frequently linked to Key and Reflection, bridging the gap between disciplines.
He has researched Point cloud in several fields, including Process, Vertex, Point, Skeleton and Laser scanning.
His studies deal with areas such as Sketch and 3D reconstruction as well as Computer graphics.
His Rendering research integrates issues from Pixel, Pipeline transport, Voxel and Texture mapping.
His most cited work include:
- PointCNN: convolution on Χ -transformed points (615 citations)
- Apparatus and method for volume processing and rendering (333 citations)
- Build-to-last: strength to weight 3D printed objects (216 citations)
What are the main themes of his work throughout his whole career to date?
Baoquan Chen focuses on Artificial intelligence, Computer vision, Computer graphics, Pattern recognition and Rendering.
His Artificial intelligence course of study focuses on Set and Algorithm.
Computer vision is often connected to Representation in his work.
Baoquan Chen combines subjects such as Image processing and Visualization with his study of Computer graphics.
In the field of Rendering, his study on Real-time rendering, 3D rendering, Volume rendering and Parallel rendering overlaps with subjects such as Tiled rendering.
His Image research includes elements of Smoothing, Theoretical computer science, Operator, Base and Parameterized complexity.
He most often published in these fields:
- Artificial intelligence (57.92%)
- Computer vision (40.54%)
- Computer graphics (17.37%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (57.92%)
- Computer vision (40.54%)
- Pattern recognition (12.74%)
In recent papers he was focusing on the following fields of study:
Baoquan Chen spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Artificial neural network and Deep learning.
His study in 3D reconstruction, Generative grammar, Feature, Training set and Benchmark are all subfields of Artificial intelligence.
His study in Computer vision is interdisciplinary in nature, drawing from both Generative model and Computer animation.
His Pattern recognition study integrates concerns from other disciplines, such as Visualization, Data visualization, Sequence and Interpolation.
Within one scientific family, Baoquan Chen focuses on topics pertaining to Set under Data visualization, and may sometimes address concerns connected to Algorithm, Identification and Similarity.
His Artificial neural network research includes themes of Image and Cloning.
Between 2018 and 2021, his most popular works were:
- GRAINS: Generative Recursive Autoencoders for INdoor Scenes (71 citations)
- Online Data Organizer: Micro-Video Categorization by Structure-Guided Multimodal Dictionary Learning (51 citations)
- Deep Video‐Based Performance Cloning (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Baoquan Chen mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Artificial neural network and 3D reconstruction.
His biological study spans a wide range of topics, including Sequence, Set and Component.
Baoquan Chen interconnects Visualization, Data visualization and Deep learning in the investigation of issues within Pattern recognition.
His primary area of study in Computer vision is in the field of Motion analysis.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Image processing, Image, Base and Parameterized complexity.
His work carried out in the field of 3D reconstruction brings together such families of science as Ground truth, Animation, Computer animation and Graphics.
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