Meng, Yue (孟岳)

I am currently doing an internship at Google.
Before that, I received an M.S. degree in ECE at UC San Diego and worked in ERL and WCSNG.
I obtained my B.S. degree in the Department of Automation from Tsinghua University.



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Localization and Mapping using Instance-specific Mesh Models

Q. Feng, Y. Meng, M. Shan and N. Atanasov
IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2019.

This paper focuses on building semantic maps, containing object poses and shapes, using a monocular camera. Our contribution is an instance-specific mesh model of object shape that can be optimized online based on semantic information extracted from camera images.


SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception

Y. Meng, Y. Lu, A. Raj, S. Sunarjo, R. Guo, T. Javidi, G. Bansal and D. Bharadia
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019.
[arXiv][page] [code]

This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. SIGNet is shown to improve upon the state of art unsupervised learning for geometry perception by 30%


Dense Spatial Segmentation from Sparse Semantic Information

Q. Feng, Y. Meng and N. Atanasov
Workshop at Robotics: Science and Systems (RSS), 2018.

This paper develops an environment representation that affords reasoning about the occupancy of space, necessary for safe navigation, and about the identity of objects, necessary for complex task interpretation.