Academic Career and Research Areas
Prof. Dai's research focuses on 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.
Prof. Dai received her PhD in computer science from Stanford in 2018 and her BSE in computer science from Princeton in 2013. Her research has been recognized through a ZDB Junior Research Group Award, an ACM SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention, as well as a Stanford Graduate Fellowship. Since 2020, she has been a professor at TUM, leading the 3D AI Lab.
- Honorable Mention; ACM SIGGRAPH Outstanding Doctoral Dissertation Award (2019)
- ZDB Junior Research Group Award (2019-)
- Stanford Graduate Fellowship, Professor Michael J. Flynn Fellow (2013-2018)
- Program in Applied and Computational Mathematics Certificate Prize, Princeton University (2013)
- Google Anita Borg Memorial Scholar (2012)
Key Publications (all publications)
Kuo W, Angelova A, Lin T-Y, Dai, A: "Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve". Proceedings of the European Conference on Computer Vision. 2020.Abstract
Dai A, Ritchie D, Bokeloh M, Reed S, Sturm J, Nießner M: "Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.Abstract
Dai A, Nießner M: "3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation". Proceedings of the European Conference on Computer Vision. 2018.Abstract
Dai A, Nießner M, Zollhöfer M, Izadi S, Theobalt C: "Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration". ACM Transactions on Graphics. 2017.Abstract
Dai A, Chang A, Savva M, Halber M, Funkhouser T, Nießner M: "Scannet: Richly-annotated 3d reconstructions of indoor scenes". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.Abstract