Academic Career and Research Areas

Dr. Dai completed her Ph.D. in Computer Science at Stanford University, advised by Pat Hanrahan. During her PhD, she has advanced real-time 3D reconstruction, and leveraged this towards developing machine learning approaches towards improving the reconstruction quality and semantic and instance understanding of these 3D scans. Dr. Dai received her Bachelors degree in Computer Science from Princeton University. Her work has been recognized with a Professor Michael J. Flynn Stanford Graduate Fellowship and a ZD.B Junior Research Group.

Dr. Dai’s research aims towards attaining a 3D understanding of the world around us, capturing and constructing semantically-informed 3D models of real-world environments. This includes 3D reconstruction and semantic understanding from commodity RGB-D sensor data, leveraging generative 3D deep learning towards enabling understanding and interaction with 3D scenes for content creation and virtual or robotic agents.

Awards

  • Honorable Mention; ACM SIGGRAPH Outstanding Doctoral Dissertation Award (2019)
  • ZDB Junior Research Group (2019)
  • Stanford Graduate Fellowship (2013-2018)
  • Princeton PACM Certificate Prize (2013)
  • Google Anita Borg Memorial Scholar (2012)

A. Dai, D. Ritchie, M. Bokeloh, S. Reed, J. Sturm, and M. Nießner. Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2018.

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A. Dai and M. Nießner. 3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation. In Proceedings of the European Conference on Computer Vision (ECCV), 2018.

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A. Dai, M. Nießner, M. Zollhöfer, S. Izadi, and C. Theobalt. Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface re-integration. ACM Transactions on Graphics 2017 (TOG), 2017.

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A. Dai, C. R. Qi, and M. Nießner. Shape completion using 3d-encoder-predictor cnns and shape synthesis. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2017

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A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2017.

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