DEPT: Depth Estimation by Parameter Transfer for Single Still Images

  • Xiu Li
  • Hongwei QinEmail author
  • Yangang Wang
  • Yongbing Zhang
  • Qionghai Dai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)


In this paper, we propose a new method for automatic depth estimation from color images using parameter transfer. By modeling the correlation between color images and their depth maps with a set of parameters, we get a database of parameter sets. Given an input image, we compute the high-level features to find the best matched image sets from the database. Then the set of parameters corresponding to the best match are used to estimate the depth of the input image. Compared to the past learning-based methods, our trained database only consists of trained features and parameter sets, which occupy little space.We evaluate our depth estimation method on the benchmark RGB-D (RGB + depth) datasets. The experimental results are comparable to the state-of-the-art, demonstrating the promising performance of our proposed method.


Input Image Markov Random Field Depth Estimation Parameter Transfer Color Patch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by National Natural Science Foundation of China (Grant No. 71171121/61033005) and National 863 High Technology Research and Development Program of China (Grant No. 2012AA09A408).

Supplementary material

336656_1_En_4_MOESM1_ESM.pdf (45 kb)
Supplementary material (pdf 45 KB)


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xiu Li
    • 1
    • 2
  • Hongwei Qin
    • 1
    • 2
    Email author
  • Yangang Wang
    • 3
  • Yongbing Zhang
    • 1
    • 2
  • Qionghai Dai
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina
  2. 2.Graduate School at ShenzhenTsinghua UniversityBeijingChina
  3. 3.Microsoft Research AsiaBeijingChina

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