Rui Yu

I am a computer vision engineer at Apple, where I work on 3D computer vision and machine learning. Before that, I worked in DJI for about a year, working on 3D vision.

I did my PhD at University College London, where I was advised by Lourdes Agapito and Chris Russell. Before that, I obtained my BSc and MSc from Northwestern Polytechnical University in 2009 and 2012 respectively, under the supervision of Prof. Yanning Zhang and Prof. Tao Yang.

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I'm interested in computer vision, machine learning, optimization, deep learning and geometric vision. My research focus is about inferring the dynamic 3D scene structure from images or monocular video. I have also worked in multiple object detection, tracking, and synthetic aperture imaging.

We demonstrate the use of shape-from-shading (SfS) to improve both the quality and the robustness of 3D reconstruction of dynamic objects captured by a single camera.

This paper is subsumed by our IJCV submission.

We propose a novel Linear Program (LP) based formulation for solving jigsaw puzzles. In contrast to existing greedy methods, our LP solver exploits all the pairwise matches simultaneously, and computes the position of each piece/component globally.

In this paper we tackle the problem of capturing the dense, detailed 3D geometry of generic, complex non-rigid meshes using a single RGB-only commodity video camera and a direct approach.

In this paper we propose an unsupervised approach to the challenging problem of simultaneously segmenting the scene into its constituent objects and reconstructing a 3D model of the scene.

In this paper, we present a novel depth free all-in-focus SAI technique based on light field visibility analysis.

Automatically focusing and seeing occluded moving object in cluttered and complex scene is a significant challenging task for many computer vision applications. In this paper, we present a novel synthetic aperture imaging approach to solve this problem.

Automatically focusing and seeing occluded moving object in cluttered and complex scene is a significant challenging task for many computer vision applications. In this paper, we present a novel synthetic aperture imaging approach to solve this problem.

Robust detection and tracking of multiple people in clut- tered and crowded scenes with severe occlusion is a sig- nificant challenging task for many computer vision appli- cations. In this paper, we present a novel hybrid synthetic aperture imaging model to solve this problem.

Learning Dense 3D Models from Monocular Video
Rui Yu

In this thesis, we present two pieces of work for reconstructing dense generic shapes from monocular sequences. In the first work, we propose an unsupervised approach to the challenging problem of simultaneously segmenting the scene into its constituent objects and reconstructing a 3D model of the scene. In the second work, we propose a direct approach for capturing the dense, detailed 3D geometry of generic, complex non-rigid meshes using a single camera.

Real time multi-view 3d reconstruction
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We have developed an approach to accelerate 3D reconstruction by introducing a mechanism of vertices sharing during the process of traditional voxel splitting. Our system can run at 10fps with 8 cameras on a PC with configuration: i7-950 CPU, 4G RAM.

Real time vision-based UAV landing
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we developed a real-time vision-based UAV autonomous landing system. autonomous Different from traditional vision-based UAV landing guidance system, our system UAV Landing uses visible remote super brightness flashlight instead of infra-red light. Effective localization result can be as far as 400m even in a strong lighting environment. Accuracy of our localization result was demonstrated by the safe landing of our UAV without using Differential GPS. With this technology we participated AVIC Cup-International UAV Innovation Grand Prix.

Multi-camera, multi-people detection, imaging and tracking
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we developed a real-time multi-camera multi-people detection, synthetic aperture imaging and tracking system. A novel hybrid synthetic aperture imaging model was proposed to solve occlusion challenges. A network camera based hybrid synthetic aperture imaging system has been set up, and experimental results with qualitative and quantitative analysis demonstrate that the method can reliably locate and see people in challenge scene. Research results were published on CVPR 2011.

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