Yingda Yin

尹英达

Ph.D. student
School of Computer Science
Peking University

Email: yingda.yin at pku.edu.cn

yingda.yin at gmail.com

I am a Ph.D. student at School of Computer Science, Peking University, where I am honored to be advised by Prof. Baoquan Chen and co-advised by Prof. He Wang. I am also fortunate to work closely with Dr. Qingnan Fan at Tencent AI Lab.

My research interests lie in 3D computer vision, especially 3D scene understanding, visual localization and object pose estimation.

Publications

*: equal contribution; † corresponding author(s)

	SAI3D: Segment Any Instance in 3D Scenes Yingda Yin, Yuzheng Liu, Yang Xiao, Daniel Cohen-Or, Jingwei Huang, Baoquan Chen CVPR* 2024 arXiv / project page / code We introduce a zero-shot 3D instance segmentation approach that synergistically leverages geometric priors and semantic cues derived from Segment Anything Model (SAM).
	Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling Yulin Liu, Haoran Liu, Yingda Yin, Yang Wang, Baoquan Chen^†, He Wang^† CVPR* 2023 arXiv / project page / code / video We propose a discrete normalizing flow on SO(3) manifold, through which one can not only effectively express arbitrary distributions on SO(3), but also conditionally build the target distribution given input observations.
	A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation Yingda Yin, Yang Wang, He Wang^†, Baoquan Chen^†, ICLR 2023 (Spotlight Presentation, Top 8%) arXiv / project page / code We propose a novel Rotation Laplace distribution for probabilistic rotation estimation. Rotation Laplace distribution is robust to the disturbance of outliers and enforces much gradient to the low-error region, resulting in new state-of-the-art performance over both probabilistic and non-probabilistic baselines.
	Towards Accurate Active Camera Localization Qihang Fang, Yingda Yin, Qingnan Fan^†, Fei Xia, Siyan Dong, Sheng Wang, Jue Wang, Leonidas Guibas, Baoquan Chen^† * Equal contribution; ordered alphabetically. ECCV 2022 arXiv / code / video / poster We explicitly model the camera and scene uncertainty components to solve the problem of active camera localization by reinforcement learning. Our algorithm improves over the state-of-the-art Markov Localization based approaches by a large margin on the fine-scale camera pose accuracy.
	FisherMatch: Semi-Supervised Rotation Regression via Entropy-based Filtering Yingda Yin, Yingcheng Cai, He Wang^†, Baoquan Chen^†, CVPR 2022 (Oral Presentation, Top 4%) arXiv / project page / code / video / poster We propose the first general framework for semi-supervised rotation regression. Our algorithm models the uncertainty of rotation regression with the help of probabilistic modeling of SO(3), which facilitates the pseudo-label filtering in semi-supervised learning.
	Projective Manifold Gradient Layer for Deep Rotation Regression Jiayi Chen, Yingda Yin, Tolga Birdal, Baoquan Chen, Leonidas Guibas, He Wang^† CVPR 2022 arXiv / project page / code / video / poster We focus on improving the backward pass of deep rotation regression. Leveraging Riemannian optimization, we propose a SO(3) manifold-aware gradient that directly backpropagates into deep network weights. Our plug-and-play gradient layer can be applied to different rotation representations.
	Deep Reflection Prior Yingda Yin, Qingnan Fan, Dongdong Chen, Yujie Wang, Angelica Aviles-Rivero, Ruoteng Li, Carola-Bibiane Schönlieb Dani Lischinski, Baoquan Chen ArXiv 2019 arXiv We propose a learning-based approach that captures the reflection statistical prior for single image reflection removal. Our algorithm is driven by optimizing the target with joint constraints enhanced between multiple input images during the training stage, but is able to eliminate reflections only from a single input for evaluation.

Education

	Peking University Ph.D. student in Computer Applied Technology Advisor: Prof. Baoquan Chen 2019.9 - Present
	Dalian University of Technology B.E. in Information and Communication Engineering, Innovation Class 2015.9 - 2019.7 Grades: 95.40 (Rank 1/197) 2016 & 2017 & 2018 China National Scholarships

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