Manas Jyoti Buragohain
I am a software engineer at Magic Leap working on developing object pose estimation solutions for real-world objects. I completed my Master of Science in Robotics from the Robotics Institute at the University of Michigan working with Justin Johnson.
My research focuses on applying deep learning towards 3D reconstruction. I am particularly interested in exploring novel object representation that are feasible for learning-based methods.
Prior to coming to Michigan, I got my undergraduate degree at Delhi Technological University,
with a major in Electronics Engineering and a minor in Robotics.
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Sparse Neural Generative Inference Based Pose Estimation
Stanley Lewis, Manas Buragohain, Danish Syed, and Bahaa Aldeeb
Course project, EECS 542 Advanced topics in Computer Vision, Fall 2020.
Instructor: David Fouhey.
paper
A particle filter based end-to-end pose estimator where each particle learns latent embedding to infer pose, object likelihood, and re-sampling objective iteratively.
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Single Image 3D Reconstruction based on Conditional Generative Adverserial Networks
Danish Syed, Manas Jyoti Buragohain, and and Hansal Shah
Course project, EECS 504 Foundations of Computer Vision, Winter 2020.
Instructor: Andrew Owens.
paper
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code
An end-to-end conditional GAN framework for generating 3D objects from single RGB image. We achieve improved qualitative 3D reconstructions as compared to the Pixel2Mesh baseline.
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Probabilistic Data Association for Semantic SLAM with Loop Closure Detection
Manas Buragohain, Aohan Mei, Can Jiang, Owen Winship, and Yidong Du
Course project, EECS 568 Mobile Robotics, Winter 2020.
Instructor: Maani Ghaffari.
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code
We replicate and improve upon the work of Bowman et. al. with augmentations to object detection framework along with incorporation of loop closure for better offline map generation. I led implementation of the percerption part of the project and it was jointly useful for research.
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Fish species classification using graph embedding discriminant analysis
Snigdhaa Hasija*, Manas Jyoti Buragohain*, and S. Indu
CMVIT 2017
paper
A novel method based on an improved image-set matching approach, which uses Graph-Embedding Discriminant Analysis for fish species classification.
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