Jiejie Zhu, Liang Wang, Ruigang Yang and James Davis

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2008 (Oral Presentation)

Multi-Projector Display with Continuous Self-Calibration (pdf)

Jin Zhou, Liang Wang, Amir Akbarzadeh and Ruigang Yang

To appear in International Workshop on Projector-Camera Systems (PROCAMS), 2008

Real-Time Light Fall-off Stereo (pdf)

Miao Liao, Liang Wang, Ruigang Yang and Minglun Gong

IEEE International Conference on Image Processing (ICIP), 2008

Real-Time Visibility-Based Fusion of Depth Maps (pdf)

P. Merrell, A. Akbarzadeh, L. Wang, P. Mordohai, J-M. Frahm, R. Yang, D. Nister and M. Pollefeys

IEEE International Conference on Computer Vision (ICCV), 2007 (Oral Presentation)

Light Fall-off Stereo (pdf)

Miao Liao, Liang Wang, Ruigang Yang and Minglun Gong

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2007

Examplar-based Shape from Shading (pdf)

Xinyu Huang, Jizhou Gao, Liang Wang and Ruigang Yang

International Conference on 3-D Digital Imaging  and Modeling (3DIM), 2007 (Oral Presentation)

Real-time Video-based Reconstruction of Urban Environments (pdf)

P. Mordohai, J-M. Frahm, A. Akbarzadeh, B. Clipp, C. Engels, D. Gallup, P. Merrell, C. Salmi, S. Sinha, B. Talton, L. Wang, Q. Yang, H. Stewenius, H. Towles, G. Welch, R. Yang, M. Pollefeys and D. Nister

ISPRS Working Group V/4 Workshop 3D-ARCH 2007: 3D Virtual Reconstruction and Visualization of Complex Architectures, (ETH Zurich, Switzerland),  2007

High Quality Real-time Stereo using Adaptive Cost Aggregation and Dynamic Programming (pdf)

Liang Wang, Miao Liao, Minglun Gong, Ruigang Yang and David Nister

Third International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), 2006 (Oral Presentation)

How Far Can We Go with Local Optimization in Real-Time Stereo Matching (pdf)

Liang Wang, Mingwei Gong, Minglun Gong and Ruigang Yang

Third International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), 2006

Towards Urban 3D Reconstruction from Video (pdf)

A. Akbarzadeh, J.-M. Frahm, P. Mordohai, B. Clipp, C. Engels, D. Gallup, P. Merrell, M. Phelps, S. Sinha, B. Talton, L. Wang, Q. Yang, H. Stewenius, R. Yang, G. Welch, H. Towles, D. Nister and M. Pollefeys

Third International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), 2006 (Invited Paper)

Real-time Global Stereo Matching Using Hierarchical Belief Propagation (pdf)

Qingxiong Yang, Liang Wang, Ruigang Yang, Shengnan Wang, Miao Liao and David Nister

The British Machine Vision Conference (BMVC), 2006

Stereo Matching with Color-weighted Correlation, Hierarchical Belief Propagation and Occlusion Handling (pdf)

Qingxiong Yang, Liang Wang, Ruigang Yang, Henrik Stewenius and David Nister

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2006

BRDF Invariant Stereo using Light Transport Constancy (pdf)

James Davis, Ruigang Yang and Liang Wang

IEEE International Conference on Computer Vision (ICCV), 2005 (Oral Presentation)

Research Projects

Search space reduction for MRF stereo

We present an algorithm to reduce per-pixel search ranges for Markov Random Fields-based stereo algorithms. Our algorithm is based on the intuitions that reliably matched pixels need less regularization in the energy minimization and neighboring pixels should have similar disparity search ranges if their pixel values are similar. We propose a novel bi-labeling process to classify reliable and unreliable pixels that incorporate left-right consistency checks. We then propagate the reliable disparities into unreliable regions to form a complete disparity map and construct per-pixel search ranges based on the difference between the disparity map after propagation and the one computed from a winner-take-all method. Experimental results evaluated on the Middlebury stereo benchmark show our proposed algorithm is able to achieve 77% average reduction rate while preserving satisfactory accuracy. related paper [1]
 

Stereoscopic inpainting  

We present a novel algorithm for simultaneous color and depth inpainting. The algorithm takes stereo images and estimated disparity maps as input and fills in missing color and depth information introduced by occlusions or object removal. We first complete the disparities for the occlusion regions using a segmentation-based approach. The completed disparities can be used to facilitate the user in labeling objects to be removed. Since part of the removed regions in one image is visible in the other, we mutually complete the two images through 3D warping. Finally, we complete the remaining unknown regions using a depth-assisted texture synthesis technique, which simultaneously fills in both color and depth. We demonstrate the effectiveness of the proposed algorithm on several challenging data sets. related paper [1]

Fusion of time-of-flight depth and stereo

Time-of-flight range sensors have error characteristics which are complementary to passive stereo. They provide real time depth estimates in conditions where passive stereo does not work well, such as on white walls. In contrast, these sensors are noisy and often perform poorly on the textured scenes for which stereo excels. We introduce a method for combining the results from both methods that performs better than either alone. A depth probability distribution function from each method is calculated and then merged. In addition, stereo methods have long used global methods such as belief propagation and graph cuts to improve results, and we apply these methods to this sensor. Since time-of-flight devices have primarily been used as individual sensors, they are typically poorly calibrated. We introduce a method that substantially improves upon the manufacturer’s calibration. We show that these techniques lead to improved accuracy and robustness. related paper [1]

Light fall-off stereo

We present light fall-off stereo–LFS–a new method for computing depth from scenes beyond Lambertian reflectance and texture. LFS takes a number of images from a stationary camera as the illumination source moves away from the scene. Based on the inverse square law for light intensity, the ratio images are directly related to scene depth from the perspective of the light source. Using this as the invariant, we developed both local and global methods for depth recovery. Compared to previous reconstruction methods for non-lamebrain scenes, LFS needs as few as two images, does not require calibrated camera or light sources, or reference objects in the scene. related papers [1] [2]

3D urban reconstruction

The goal of this project is to build a data collection system and a processing pipeline for automatic geo-registered 3D reconstruction of urban scenes from video. The system collects multiple video streams, as well as GPS and INS measurements in order to place the reconstructed models in geo-registered coordinates. Besides high quality in terms of both geometry and appearance, we aim at real-time performance. Even though our processing pipeline is currently far from being real-time, we select techniques and we design processing modules that can achieve fast performance on multiple CPUs and GPUs aiming at real-time performance in the near future. (find more) related papers [1], [2], [3]

High quality and real-time stereo algorithms

I have been working on designing algorithms for dense two-frame stereo matching problem aiming at both high reconstruction quality and real-time performance. Evaluation using the benchmark Middlebury stereo database shows that our algorithms are among the best in terms of both quality and speed. The real-time performance mainly comes from the parallelism of today’s commodity graphics hardware. related papers [1], [2], [3], [4]

BRDF invariant stereo using light transport constancy

Nearly all existing methods for stereo reconstruction assume that scene reflectance is Lambertian and make use of brightness constancy as a matching invariant. We introduce a new invariant for stereo reconstruction called Light Transport Constancy, which allows completely arbitrary scene reflectance (BRDFs). This invariant can be used to formulate a rank constraint on multi-view stereo matching when the scene is observed by several lighting configurations, in which only the lighting intensity varies. In addition, we show that this multi-view constraint can be used with as few as two cameras and two lighting configurations. This new constraint can be used to provide BRDF invariance to any existing stereo method, whenever appropriate lighting variation is available. related papers [1], [2]

View-dependent texture splatting

We present a novel approach to render low resolution point clouds with multiple high resolution textures. The low precision, noisy, and sometimes incomplete nature of such data sets is not suitable for existing point-based rendering techniques that are designed to work with high precision and high density point clouds. Our new algorithm - View-Dependent Textured Splatting (VDTS) - combines traditional splatting with a view-dependent texturing strategy to reduce rendering artifacts caused by imprecision or noise in the input data. VDTS requires no pre-processing of input data, addresses texture aliasing, and most importantly, processes texture visibility on-the-fly. The combination of these characteristics makes VDTS well suited for interactive rendering of dynamic scenes. (find more) related paper [1]

Contact

Email: lwangd AT cs DOT uky DOT edu

Phone: +1-859-257-1257 ext 82332

Mail:      Center for Visualization and Virtual Environments
                                 University of Kentucky
                             1 Quality Street, Suite 800
                         Lexington, KY 40507-1464, USA

Web: http://www.vis.uky.edu/~wangl/