Kun Li 李坤
中文版Chinese Version

 

Associate Professor

College of Intelligence and Computing,
Tianjin University (Peiyang University) , Tianjin 300350, China

Email: lik@tju.edu.cn

PHD Advisor



Projects
Global 3D Non-Rigid Registration of Deformable Objects Using a Single RGB-D Camera
IEEE TIP 2019
We present a novel global non-rigid registration method for dynamic 3D objects. Our method allows objects to undergo large non-rigid deformations, and achieves high quality results even with substantial pose change or camera motion between views. In addition, our method does not require a template prior and uses less raw data than tracking based methods since only a sparse set of scans is needed.
Robust Non-Rigid Registration with Reweighted Position and Transformation Sparsity
IEEE TVCG 2019 Won in the SHREC 2019 Contest
We propose a robust non-rigid registration method using reweighted sparsities on position and transformation to estimate the deformations between 3-D shapes.
Spatio-Temporal Reconstruction for 3D Motion Recovery
IEEE TCSVT 2019
We address the challenge of 3D motion recovery by exploiting the spatio-temporal correlations of corrupted 3D skeleton sequences.
Tensor Completion From Structurally-Missing Entries by Low-TT-rankness and Fiber-wise Sparsity
JSTSP 2018
Most tensor completion methods assume that missing entries are randomly distributed in incomplete tensors, but this could be violated in practical applications where missing entries are not only randomly but also structurally distributed. To remedy this, we propose a novel tensor completion method equipped with double priors on the latent tensor, named tensor completion from structurally-missing entries by low tensor train (TT) rankness and fiber-wise sparsity.
Shape and Pose Estimation for Closely Interacting Persons Using Multi-view Images
PG 2018
We propose a fully-automatic markerless motion capture method to simultaneously estimate 3D poses and shapes of closely interacting people from multi-view sequences.
Image-based PM2.5 Estimation and Its Application on Depth Estimation
ICASSP 2018
We propose an image-based method for PM2.5 estimation and a depth estimation method by capturing a single image.
Intrinsic Image Decomposition With Sparse and Non-local Priors
ICME 2017 Code World’s FIRST 10K Best Paper Award – Platinum
We propose a new intrinsic image decomposition method that decomposing a single RGB-D image into reflectance and shading components.
SPA: Sparse Photorealistic Animation Using a Single RGB-D Camera
IEEE TCSVT 2017
We propose a marker-less performance capture method using sparse deformation to obtain the geometry and pose of the actor for each time instance in the database.
Video Super-resolution Using an Adaptived Superpixel-guided Auto-Regeressive Model
PR 2016 Code
We propose a video super-resolution method based on an adaptive superpixel-guided auto-regressive (AR) model.
Foreground-Background Separation From Video Clips via Motion-assisted Matrix Restoration
IEEE TCSVT 2015
We propose a motion-assisted matrix restoration (MAMR) model for foreground-background separation from video clips.
Non-Rigid Structure from Motion via Sparse Representation
IEEE Transactions on Cybernetics 2015
We propose a new approach for non-rigid structure from motion with occlusion, based on sparse representation.
Graph-based Segmentation for RGB-D Data Using 3-D Geometry Enhanced Superpixels
IEEE Transactions on Cybernetics 2015
We propose a two-stage segmentation method for RGB-D data: 1) oversegmentation by 3-D geometry enhanced superpixels; and 2) graph-based merging with label cost from superpixels.
Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model
ECCV 2012/IEEE TIP 2014 Code
We propose an adaptive color-guided autoregressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras.
Temporal-Dense Dynamic 3D Reconstruction with Low Frame Rate Cameras
IEEE JSTSP 2012
We propose a new method for temporal-densely capturing and reconstructing dynamic scenes with low frame rate cameras, which consists of spatio-temporal sampling, spatio-temporal interpolation, and spatio-temporal fusion.
Three-Dimensional Motion Estimation via Matrix Completion
IEEE TSMCB 2012
We propose a new 3D motion estimation method based on matrix completion.
Markerless Shape and Motion Capture from Multi-view Video Sequences
IEEE TCSVT 2011
We propose a new method for temporal-densely capturing and reconstructing dynamic scenes with low frame rate cameras, which consists of spatio-temporal sampling, spatio-temporal interpolation, and spatio-temporal fusion.
Multi-Camera and Multi-Lighting Dome
We construct a dome to record the geometry, texture and motion of human actors in a dedicated multiple-camera studio with controlled lighting and a chromakey background. The diameter of the dome is 6 meters which provides enough space for character perform. 40 PointGrey flea2 cameras are ring-shape arranged on the dome and 320 LEDs are evenly spaced on the hemisphere of the dome.

Links
School of Computer Science and Technology
Tianjin University (Peiyang University)