Zhixiang Min

I received my Ph.D. (2018-2023) in Computer Science at Stevens Institute of Technology, advised by Prof. Enrique Dunn. I spent summer internships at Zillow and NEC Labs.

My research interest lies in 3D computer vision (SfM, SLAM, localization, reconstruction) and recently image-based rendering. My work during PhD focuses on closing the gap between deep learning methods and 3D vision problems. Specifically, I design localization algorithms that utilize deep learning to improve challenging instances and solve ill-posed problems, while, importantly, maintaining the geometric interpretabilities.

After graduation, I joined Apple VIO/SLAM team, working on AR/VR and Vision Pro.

Email: noah.zx.min [at] gmail.com
[Google Scholar] / [LinkedIn] / [CV]

Projects

(I occasionally fix some typos in our camready submissions. Please refer to our arxiv version for the best experience.)

	Geometric Viewpoint Learning with Hyper-Rays and Harmonics Encoding Zhixiang Min, Juan Carlos Dibene, Enrique Dunn ICCV 2023 [thecvf] [appendix]
	General Planar Motion from a Pair of 3D Correspondences Juan Carlos Dibene, Zhixiang Min, Enrique Dunn ICCV 2023 (Oral Presentation) [thecvf] [appendix] [video(6:30)]
	NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization Zhixiang Min, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Enrique Dunn, Manmohan Chandraker CVPR 2023 [arxiv] [appendix] [video]
	LASER: LAtent SpacE Rendering for 2D Visual Localization Zhixiang Min, Naji Khosravan, Zachary Bessinger, Manjunath Narayana, Sing Bing Kang, Enrique Dunn, Ivaylo Boyadzhiev CVPR 2022 (Oral Presentation) [arxiv] [appendix] [video] [code]
	VOLDOR-SLAM: For the times when feature-based or direct methods are not good enough Zhixiang Min, Enrique Dunn ICRA 2021 [arxiv] [video] [code] Our SLAM solution has achieved 2nd place in both tracks (monocular, stereo) of CVPR'20 SLAM Challenge while retaining realtime.
	VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals Zhixiang Min, Yiding Yang, Enrique Dunn CVPR 2020 (Oral Presentation) [arxiv] [appendix] [video] [code]