What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer graphics
His primary areas of study are Artificial intelligence, Rendering, Computer vision, Algorithm and Computer graphics.
His research on Artificial intelligence frequently links to adjacent areas such as Pattern recognition.
His Rendering research includes themes of CUDA, Graphics pipeline and Computer graphics.
His work in Computer vision addresses issues such as Representation, which are connected to fields such as Optimization problem, Sketch, Polygon mesh and Normal mapping.
As a part of the same scientific family, Rui Wang mostly works in the field of Algorithm, focusing on Precomputed Radiance Transfer and, on occasion, Visibility, Product, Exponentiation and Basis.
His Computer graphics study combines topics from a wide range of disciplines, such as Pixel, Bidirectional reflectance distribution function and Light field.
His most cited work include:
- Real-time KD-tree construction on graphics hardware (375 citations)
- 3D Shape Induction from 2D Views of Multiple Objects (114 citations)
- Real-time soft shadows in dynamic scenes using spherical harmonic exponentiation (98 citations)
What are the main themes of his work throughout his whole career to date?
Rui Wang focuses on Artificial intelligence, Rendering, Computer vision, Computer graphics and Algorithm.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Pattern recognition.
His research integrates issues of Tree, Voxel and Generative grammar in his study of Pattern recognition.
He interconnects Ray tracing and Graphics in the investigation of issues within Rendering.
The concepts of his Computer vision study are interwoven with issues in Bidirectional reflectance distribution function and Shading.
His work on Computation as part of general Algorithm study is frequently linked to Colors of noise and Adaptive sampling, bridging the gap between disciplines.
He most often published in these fields:
- Artificial intelligence (47.83%)
- Rendering (37.68%)
- Computer vision (35.51%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (47.83%)
- Rendering (37.68%)
- Topology (9.42%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Rendering, Topology, Algorithm and Pattern recognition.
The study of Artificial intelligence is intertwined with the study of Computer vision in a number of ways.
His Computer vision research incorporates elements of Pipeline and Shading.
The Rendering study combines topics in areas such as Ray tracing and Visibility.
His work on Base and Boundary as part of his general Topology study is frequently connected to Hexahedron and Decomposition, thereby bridging the divide between different branches of science.
His study in Algorithm is interdisciplinary in nature, drawing from both Training set and Radiance.
Between 2019 and 2021, his most popular works were:
- Learning Generative Models of Shape Handles (4 citations)
- Inferring 3D Shapes from Image Collections Using Adversarial Networks (3 citations)
- Adaptive Incident Radiance Field Sampling and Reconstruction Using Deep Reinforcement Learning (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Computer graphics
Rui Wang mostly deals with Artificial intelligence, Algorithm, Point cloud, Pattern recognition and Generative model.
His study connects Computational biology and Artificial intelligence.
His Algorithm study incorporates themes from Global illumination, Path tracing, Reconstruction algorithm and Radiance.
His Point cloud study combines topics in areas such as Feature learning, Segmentation, Regular polygon and Ground truth segmentation.
His biological study spans a wide range of topics, including Adversarial system, Sensory cue, Differentiable function and Generative grammar.
His Generative model research incorporates themes from Theoretical computer science, Parsing, Leverage and Solid modeling.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
