What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of study are Artificial intelligence, Computer vision, Algorithm, Computer graphics and Interpolation.
Artificial intelligence and Pattern recognition are commonly linked in his work.
Hujun Bao interconnects Iterative method and Deformation in the investigation of issues within Computer vision.
His Algorithm study integrates concerns from other disciplines, such as Basis, Precomputed Radiance Transfer, Solver, Mathematical optimization and Texture mapping.
His studies in Computer graphics integrate themes in fields like 3d model, Material transfer and Graphics processing unit.
His Interpolation research is multidisciplinary, incorporating elements of Global illumination, Rendering and Bilinear interpolation.
His most cited work include:
- Mesh editing with poisson-based gradient field manipulation (473 citations)
- Locality sensitive discriminant analysis (413 citations)
- Large mesh deformation using the volumetric graph Laplacian (342 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Algorithm, Computer graphics and Rendering.
Hujun Bao regularly links together related areas like Pattern recognition in his Artificial intelligence studies.
His research combines Computer graphics and Computer vision.
The study incorporates disciplines such as Polygon mesh, Interpolation, Surface and Solver, Mathematical optimization in addition to Algorithm.
His Polygon mesh research includes elements of Mesh generation, Laplacian smoothing, Texture mapping and Topology.
Hujun Bao has included themes like Tiled rendering and Software rendering in his Rendering study.
He most often published in these fields:
- Artificial intelligence (52.82%)
- Computer vision (43.66%)
- Algorithm (19.01%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (52.82%)
- Computer vision (43.66%)
- Pattern recognition (8.45%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Pattern recognition, Robustness and Pixel are his primary areas of study.
His study in Image, Pose, Object, Artificial neural network and Representation is carried out as part of his studies in Artificial intelligence.
Hujun Bao works mostly in the field of Computer vision, limiting it down to topics relating to Expression and, in certain cases, Skinning, Construct, Polygon mesh and Animation.
His Pattern recognition research includes themes of Differentiable function, Convolution, Similarity and Structure.
As a part of the same scientific family, Hujun Bao mostly works in the field of Robustness, focusing on Inference and, on occasion, Source code, Visualization, Digitization and Stereopsis.
His work carried out in the field of Pixel brings together such families of science as Epipolar geometry and Pipeline.
Between 2018 and 2021, his most popular works were:
- PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation (194 citations)
- Depth Completion From Sparse LiDAR Data With Depth-Normal Constraints (38 citations)
- A Late Fusion CNN for Digital Matting (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Hujun Bao mostly deals with Artificial intelligence, Computer vision, Robustness, Artificial neural network and Algorithm.
Many of his studies involve connections with topics such as Pattern recognition and Artificial intelligence.
His work focuses on many connections between Computer vision and other disciplines, such as Expression, that overlap with his field of interest in Skinning, Polygon mesh, Representation and Construct.
Hujun Bao focuses mostly in the field of Robustness, narrowing it down to matters related to Pixel and, in some cases, Point and Shooting range.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Feature extraction, Inference, Audio signal and Source code.
His Algorithm study combines topics in areas such as Global illumination, Rendering, Lidar, Lidar data and Normal.
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