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Si Lu | ||||||||||||||||||||||||||
Computer Vision Engineer -
Autonomous Vehicles at NVIDIA, CA Ph.D. Email: lusi "at" pdx.edu Resume (updated on 09-19-2019) |
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Working on Autonomous Driving at NVIDIA. PhD student in Computer Science at Portland State University. My advisor is Dr. Feng Liu. I have More than 9 years' experience on Computer Vision & Graphics topics such as 3D construction/enhancement in light field VR, novel view synthesis for camera arrays, video stabilization, image stitching as well as computational photography such as image/RGBD denoising and patch matching. Passionate about developing/implementing state-of-the-art computer vision and computer graphics algorithms. Skilled programmer with C/C++/Python/Matlab/OpenCV/OpenGL/Pytorch. Familiar with machine learning and deep learning techniques such as CNN/RNN/LSTM/ResNet/DenseNet/Faster R-CNN/Mask R-CNN. | |||||||||||||||||||||||||||
Enducation
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Publications (Click here to see my peer-review expericen) | |||||||||||||||||||||||||||
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Experience | |||||||||||||||||||||||||||
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![]() I am proud that my work appeared in the GTC keynote demo at GTC 2019. Click to watch. ![]() NVIDIA teamed up with leading HD mapping partners to deliver a global mass market end-to-end autopilot solution. Click to watch. |
Autonomous Vehicles:
HD Map
Generation
Autonomous Vehicles:
Localization |
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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 |
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Teaching Experience | |||||||||||||||||||||||||||
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Academic Peer-Reviewing experience | |||||||||||||||||||||||||||
• IEEE Transactions on Image
Processing,
Lightening Network for Low-Light Image Enhancement(in progress)
• IntelliSys2020, Recommender - A Personalized Subreddit Recommendation Engine • IntelliSys2020, Perspectives of Applying the e-Learning Model in Educational Institutions in Bosnia and Herzegovina • IntelliSys2020, Neural Network Modeling of Productive Intellectual Activity in Older Adolescents • IntelliSys2020, AI Scrum Master - An automated approach for agile project planning based on artificial intelligence • IntelliSys2020, Enhanced Cognition aided by Reinforced Learning and Real-Time Decision over Shared-Spectrum • IEEE Access, A Novel Bayesian Patch-Based Approach for Image Denoising • IEEE Transactions on Image Processing, Spatial-temporal Networks for Temporally Coherent Video Super-Resolution • IEEE Transactions on Image Processing, FusionNet: An Unsupervised Convolutional Variational Network for Hyperspectral and Multispectral Image Fusion • IEEE Transactions on Image Processing, A Multi-path Deep Neural Network for Visual Smoke Surveillance • IEEE Transactions on Image Processing, Automatic Content-preserving and Temporal-consistent 360° Video Projection • IEEE Transactions on Multimedia, A Multi-Attribute Blind Quality Evaluator for Tone-mapped images • IEEE Transactions on Multimedia, No-reference Image Quality Assessment via Local Structural Information Representation • IEEE Transactions on Multimedia, Visual Importance and Distortion Guided Deep Image Quality Assessment Framework • IEEE Signal Processing Letters, Color Image Denoising via Cross-channel Texture Transferring • IEEE Signal Processing Letters, A geometric-separating convolutional neural network for blind image inpainting • IEEE Transactions on Circuits and Systems for Video Technology, Depth Estimation Using an Infrared Dot Projector and an Infrared Color Stereo Camera • IEEE Transactions on Circuits and Systems for Video Technology, Deep Convolutional Shearlet Transform for Densely-Sampled Light Field Reconstruction • IEEE Signal Processing Letters, Strong Edge-Aware Depth Image Completion with Multidirectional Filtering • ACCV Workshop on RGB-D-Sensing and understanding via combined colour and depth, Unsupervised RGBD Video Object Segmentation Using GANs • ACCV Workshop on RGB-D-Sensing and understanding via combined colour and depth, Environment-aware non-rigid registration in surgery using physics-based simulation |