Name
Abstract | ||
|---|---|---|
The rapid development of 3D technology and computer vision applications has motivated a thrust of methodologies for depth acquisition and estimation. However, existing hardware and software acquisition methods have limited performance due to poor depth precision, low resolution, and high computational cost. In this paper, we present a computationally efficient method to estimate dense depth maps from sparse measurements. There are three main contributions. First, we provide empirical evidence that depth maps can be encoded much more sparsely than natural images using common dictionaries, such as wavelets and contourlets. We also show that a combined wavelet-contourlet dictionary achieves better performance than using either dictionary alone. Second, we propose an alternating direction method of multipliers (ADMM) for depth map reconstruction. A multiscale warm start procedure is proposed to speed up the convergence. Third, we propose a two-stage randomized sampling scheme to optimally choose the sampling locations, thus maximizing the reconstruction performance for a given sampling budget. Experimental results show that the proposed method produces high-quality dense depth estimates, and is robust to noisy measurements. Applications to real data in stereo matching are demonstrated. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIP.2015.2409551 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
depth map reconstruction,alternating direction method of multipliers,image matching,stereo matching,wavelet transforms,sparse reconstruction,learning (artificial intelligence),sampling location,two-stage randomized sampling scheme,depth acquisition methodology,image reconstruction,software acquisition method,dense depth map estimation,compressed sensing,wavelet,computer vision application,random sampling,depth estimation methodology,sampling budget,computer vision,3d technology,admm,wavelet-contourlet dictionary,sampling methods,hardware acquisition method,stereo image processing,contourlet,natural image,disparity estimation,optimization,dictionaries,learning artificial intelligence,estimation,hardware | Convergence (routing),Computer science,Artificial intelligence,Depth map,Contourlet,Wavelet,Speedup,Warm start,Computer vision,Pattern recognition,Algorithm,Sampling (statistics),Thrust | Journal |
Volume | Issue | ISSN |
24 | 6 | 1941-0042 |
Citations | PageRank | References |
14 | 0.81 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lee-kang Liu | 1 | 16 | 2.52 |
| Stanley H. Chan | 2 | 403 | 30.95 |
| Truong Q Nguyen | 3 | 14 | 1.15 |