Ziyi Wang

I am a third year PhD student in the Department of Automation at Tsinghua University, advised by Prof. Jiwen Lu . In 2020, I obtained my B.Eng. in the Department of Electronic Engineering, Tsinghua University. I also obtained B.Admin. as dual degree in the School of Ecnomics and Management, Tsinghua University.

I am broadly interested in computer vision and deep learning. My current research focuses on 3D vision.

Email / Google Scholar / Github

News

2022-09: 1 paper on 3D prompt learning is accepted to NeurIPS 2022.

2022-03: 1 paper on 3D semantic segmentation is accepted to CVPR 2022.

2021-07: 2 papers (including 1 oral) are accepted to ICCV 2021.

2021-03: 1 paper on 3D scene flow estimation is accepted to CVPR 2021.

Publications

* indicates equal contribution

	P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting Ziyi Wang, Xumin Yu, Yongming Rao, Jie Zhou , Jiwen Lu Conference on Neural Information Processing Systems (NeurIPS), 2022 Spotlight* [arXiv] [Code] [Project Page] [中文解读] P2P is a framework to leverage large-scale pre-trained image models for 3D point cloud analysis.
	SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation Ziyi Wang, Yongming Rao, Xumin Yu, Jie Zhou , Jiwen Lu IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 [arXiv] [Code] We present Semantic-Affine Transformation that transforms decoder mid-level features of the encoder-decoder segmentation network with class-specific affine parameters.
	PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu , Jie Zhou IEEE International Conference on Computer Vision (ICCV), 2021 Oral Presentation [arXiv] [Code] [中文解读] PoinTr is a transformer-based framework that reformulates point cloud completion as a set-to-set translation problem.
	Towards Interpretable Deep Metric Learning with Structural Matching Wenliang Zhao, Yongming Rao, Zyi Wang, Jiwen Lu , Jie Zhou IEEE International Conference on Computer Vision (ICCV), 2021 [arXiv] [Code] We present a deep interpretable metric learning (DIML) that adopts a structural matching strategy to explicitly aligns the spatial embeddings by computing an optimal matching flow between feature maps of the two images.
	PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds Yi Wei , Ziyi Wang, Yongming Rao, Jiwen Lu , Jie Zhou IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)*, 2021 [arXiv] [Code] We present point-voxel correlation fields for 3D scene flow estimation which migrates the high performance of RAFT and provides a solution to build structured all-pairs correlation fields for unstructured point clouds.

Honors and Awards

2021 Haining Talent Scholarship, Tsinghua University

2020 Excellent graduation thesis, Tsinghua University

2018 Zheng Geru Scholarship, Tsinghua University

2017 Hongqian Electronics Scholarship, Tsinghua University

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