What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Computer graphics
Artificial intelligence, Computer vision, Iterative reconstruction, Computer graphics and Graphics hardware are her primary areas of study.
Sudipta N. Sinha conducted interdisciplinary study in her works that combined Artificial intelligence and Piecewise.
In her research, Photo-consistency is intimately related to Minimum cut, which falls under the overarching field of Computer vision.
Her work carried out in the field of Iterative reconstruction brings together such families of science as 3D reconstruction and Global Positioning System.
Her Global Positioning System research incorporates elements of Image processing and Robustness.
Her Graphics hardware research integrates issues from Video tracking, OpenGL, Central processing unit and Graphics processing unit.
Her most cited work include:
- Detailed Real-Time Urban 3D Reconstruction from Video (650 citations)
- Real-Time Seamless Single Shot 6D Object Pose Prediction (341 citations)
- GPU-based Video Feature Tracking And Matching (264 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Artificial intelligence, Computer vision, Structure from motion, Computer graphics and Iterative reconstruction.
Her Artificial intelligence study frequently draws connections to adjacent fields such as Pattern recognition.
The various areas that she examines in her Structure from motion study include Orientation, Vanishing point, Pairwise comparison and Bundle adjustment.
Her work deals with themes such as Texture, Panorama and Immersion, which intersect with Computer graphics.
Her work investigates the relationship between Iterative reconstruction and topics such as Video tracking that intersect with problems in Graphics hardware.
The 3D reconstruction study combines topics in areas such as Developable surface and Global Positioning System.
She most often published in these fields:
- Artificial intelligence (89.29%)
- Computer vision (75.89%)
- Structure from motion (17.86%)
What were the highlights of her more recent work (between 2017-2020)?
- Artificial intelligence (89.29%)
- Computer vision (75.89%)
- Convolutional neural network (8.93%)
In recent papers she was focusing on the following fields of study:
Sudipta N. Sinha focuses on Artificial intelligence, Computer vision, Convolutional neural network, Pose and Feature.
In general Artificial intelligence study, her work on Image, Pixel and Representation often relates to the realm of Function and Viewpoints, thereby connecting several areas of interest.
She integrates many fields, such as Computer vision and Point, in her works.
Sudipta N. Sinha interconnects Depth map, Inpainting, Noise, RGB color model and Ground truth in the investigation of issues within Convolutional neural network.
Sudipta N. Sinha has researched Pose in several fields, including Mixed reality, Augmented reality, Object detection and Deep learning.
The study incorporates disciplines such as Visualization, Feature extraction, Pattern recognition and Server in addition to Feature.
Between 2017 and 2020, her most popular works were:
- Real-Time Seamless Single Shot 6D Object Pose Prediction (341 citations)
- Revealing Scenes by Inverting Structure From Motion Reconstructions (47 citations)
- Photorealistic Image Synthesis for Object Instance Detection (31 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Her primary scientific interests are in Artificial intelligence, Computer vision, Pose, 3D reconstruction and Convolutional neural network.
Her biological study spans a wide range of topics, including Developable surface and Surface reconstruction.
Her Computer vision study frequently links to adjacent areas such as Computer graphics.
Her Pose research includes themes of Mixed reality, Augmented reality, Pascal and Deep learning.
She combines subjects such as Active vision, Representation and Noise with her study of 3D reconstruction.
Her Convolutional neural network research incorporates themes from Ground truth, Real image and Rendering.
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.
