What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Optics
Ping Tan mainly focuses on Artificial intelligence, Computer vision, Image, Pattern recognition and Structure from motion.
His Pixel, Image stabilization and Motion estimation study in the realm of Artificial intelligence interacts with subjects such as Domain and Context model.
His research combines Robustness and Computer vision.
His Image research incorporates elements of Segmentation and The Internet.
The Pattern recognition study combines topics in areas such as DUAL, Translation, Image translation, Task and Function.
In his research on the topic of Structure from motion, Trifocal tensor, Image registration and Bundle adjustment is strongly related with Pose.
His most cited work include:
- DualGAN: Unsupervised Dual Learning for Image-to-Image Translation (866 citations)
- Sketch2Photo: internet image montage (534 citations)
- DualGAN: Unsupervised Dual Learning for Image-to-Image Translation (234 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, Image, Pattern recognition and Photometric stereo.
His work is connected to Feature, Pixel, Segmentation, Deep learning and Benchmark, as a part of Artificial intelligence.
His Computer vision research incorporates themes from Reflectivity and Robustness.
In the subject of general Image, his work in Image processing is often linked to Set, Process and Quality, thereby combining diverse domains of study.
His Pattern recognition research includes elements of Object and Task.
Ping Tan interconnects Isotropy, Euclidean geometry, Normal and Radiometry in the investigation of issues within Photometric stereo.
He most often published in these fields:
- Artificial intelligence (86.79%)
- Computer vision (69.81%)
- Image (19.50%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (86.79%)
- Computer vision (69.81%)
- Pattern recognition (14.47%)
In recent papers he was focusing on the following fields of study:
Ping Tan spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Generalization and Convolutional neural network.
His Image, Point cloud, Augmented reality, Feature and Rendering investigations are all subjects of Artificial intelligence research.
His research in Image intersects with topics in Range and Product.
His Computer vision study combines topics from a wide range of disciplines, such as Deep learning and Benchmark.
His study in the field of Feature learning also crosses realms of Encoder.
His Convolutional neural network study integrates concerns from other disciplines, such as Algorithm and Normalization.
Between 2019 and 2021, his most popular works were:
- Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching (46 citations)
- Intrinsic Image Decomposition with Step and Drift Shading Separation (10 citations)
- Self-Supervised Human Depth Estimation From Monocular Videos (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Topology
Ping Tan spends much of his time researching Artificial intelligence, Computer vision, Algorithm, Generalization and Normalization.
His Artificial intelligence study frequently draws parallels with other fields, such as Belief propagation.
His Computer vision research is multidisciplinary, incorporating elements of Perspective, Reflectivity and Benchmark.
His Perspective research includes themes of Image, Face and Iterative reconstruction.
The Decoding methods research Ping Tan does as part of his general Algorithm study is frequently linked to other disciplines of science, such as Micrography, therefore creating a link between diverse domains of science.
His Generalization study combines topics in areas such as Motion, Training set, Ground truth and Monocular.
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