Si Lu
Ph.D.
Department of Computer Science Maseeh College
of Engineering and Computer Science
P.O. Box 751
Portland State University
Portland, OR 97207-0751
Email: lusi "at" pdx.edu
Resume
 
I'm a research assistant at Computer Graphics & Vision Lab. Graduating soon and seeking for full-time positions in Computer Vision, Machine Learning/Deep Learning as well as Computational Photography. My advisor is Prof. Feng Liu. I received my B.E. and M.S. degree from Tsinghua University. More than 9 years' experience on Computer Vision and Graphics topics such as novel view synthesis, video stabilization, image stitching, object detection and computational photography such as image denoising and inpainting. Passionate about developing and implementing novel computer vision algorithms. Skilled in implementing state-of-the-art computer vision/image processing algorithms. Skilled programmer with C/C+ +/Matlab/OpenCV/OpenGL. Familiar with CNN/ResNet/DenseNet/Faster RCNN/Mask RCNN.
 
Publication
Si Lu, Xiaofeng Ren, and Feng Liu. Depth Enhancement via Low-rank Matrix Completion. 
IEEE CVPR 2014, Columbus, OH, June 2014. Project website. Poster.
 
Experience
 
Computer Vision Intern - Lytro Immerge (VR) Moutain View, CA, Jun., 2017 - Sep., 2017
  Multi-view 3D reconstruction ( C/C++/OpenCV )
• Processing HD (2048×2048) RGB-D images
Signigicant fine detal improvements for VR depth maps (left)
• Depth enhancement for light filed (multi-view) camera arrays (middle)
• Depth enchancement for stiched 360-degree videos in Virtual Reality (right)
• Integrated into Lytro Immerge (VR) production pipeline for depth
 
Projects
Highspeed Videos from Camera Arrays (2016-2017)
• Develope an algorithm to generate highspeed videos from camera arrays
• Use novel views synthesis to do frame interpolation among cameras 
• Use image-based rendering techniques, e.g., feature matching/warping
• Plausible and parallax-free novel views can be robustly interpolated
• Works for challenging scenes with large camera and object motions
• Submitted to CVPR 2018 (Video demo)
   
   Patch Matching for Image Denoising (2017-present)
Develope a clustering-based approach to consistently improve patch-based denoising techniques' (like BM3D) performance by using learned patch descriptors trained via deep learning. (in progress, submitting to ECCV 2018)
   
Depth Enhancement (2012-2014)
Develop a depth map enhancement algorithm that performs depth map completion and denoising simultaneously for RGBD cameras like Microsoft Kinect and Xtions Pro. Project website. Poster.
   
Flash Light Detection for Unmanned Aerial Vehicles (2015)
Developed an algorithm that allows real-time flash light detection for Unmanned Aerial Vehicles. By designing specific color-based features, we achieved a 89.2% detection rate with a small false positive rate (0.6%).
 
Robocup Team Leader (2007-2012) Project website.
• Developed a robot vision system with accurate and real-time (20 FPS) objects (football, field lines and goals) detection and ball-locating/self-locating (C++/Windows Mobile SDK/Linux).
Developed a wireless vision parameter tuning interface to allow fast camera calibration before real games. By utilizing this interface, I reduced the parameter tuning time by more than 70% before each game (Matlab, C).
 
Teaching
CS 447/547-Computer Graphics (instructor): teach Computer Graphics topics on movies, games, animations and 3d rendering. Helped students with two course projects - a mini-Photoshop (FLTK/OpenCV) with basic image processing and an Amusement Park 3D animation rendering project (OpenGL).
CS 410/510-Computational Photography (instructor): teach research topics ranging from concepts of digital camera and photography to computer vision/graphics techniques, including high dynamic range imaging, panorama stitching, image segmentation & matting, video stabilization, virtual reality basics, deep learning in computer vision etc..