Ziyi Wang

I am a fourth 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.

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Publications

* indicates equal contribution

P2P++ is the extended journal version of P2P. We further propose Pixel-to-Point Distillation to make P2P applicable in scene-level perception tasks.

DPV-RAFT is the extended journal version of PV-RAFT. We further propose Spatial Deformation and Temporal Deformation to enhance PV-RAFT.

TAP is a 3D-to-2D generative pre-training method that generate projected images of point clouds from instructed perspectives.

P2P is a framework to leverage large-scale pre-trained image models for 3D point cloud analysis.

We present Semantic-Affine Transformation that transforms decoder mid-level features of the encoder-decoder segmentation network with class-specific affine parameters.

PoinTr is a transformer-based framework that reformulates point cloud completion as a set-to-set translation problem.

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.

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.

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