Cross-view Semantic Segmentation for Sensing Surroundings

Bowen Pan1, Jiankai Sun2, Ho Yin Tiga Leung2, Alex Andonian1, Bolei Zhou2

1 Massachusetts Institute of Technology

2 The Chinese University of Hong Kong

[Paper] [Code] [Supplemental Materials] [1-min Demo Video]

We introduce a novel spatial understanding task calls Cross-view Semantic Segmentation. In this task, top-down-view semantics are predicted from the first-view real-world observations. Input observations from multiple angles are fused together.


In this framework, the View Parsing Network (VPN) is proposed to parse the first-view observations into a top-down-view semantic map indicating the spatial location of all the objects at pixel-level. The view transformer module contained in VPN is designed to aggregate the surrounding information collected from first-view observations in multiple angles and modalities. To mitigate the issue of lacking real-world annotations, we train the VPN in simulation environment and utilize the off-the-shelf domain adaptation technique to transfer it to real-world data. We evaluate our VPN on both synthetic and real-world data. The experimental results show that our model can effectively make use of the information from different views and multi-modalities. Thus the proposed VPN is able to accurately predict the top-down-view semantic mask of the visible objects as well as barely seen objects, in both synthetic and real-world environments.



Framework of the View Parsing Network for cross-view semantic segmentation. The simulation part illustrates the architecture and training scheme of our VPN. And the real-world part demonstrates our domain adaptation process for transferring our VPN to the real world.

Demo Video


     author={B. {Pan} and J. {Sun} and H. Y. T. {Leung} and A. {Andonian} and B. {Zhou}},
     journal={IEEE Robotics and Automation Letters},
     title={Cross-View Semantic Segmentation for Sensing Surroundings},
B. Pan, J. Sun, H. Y. T. Leung, A. Andonian and B. Zhou, "Cross-View Semantic Segmentation for Sensing Surroundings," in IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4867-4873, July 2020, doi: 10.1109/LRA.2020.3004325.
B. Pan, J. Sun, H. Y. T. Leung, A. Andonian and B. Zhou, "Cross-View Semantic Segmentation for Sensing Surroundings," in IEEE International Conference on Intelligent Robots and Systems (IROS) 2020.