Learning Depth with Convolutional Spatial Propagation Network

Xinjing Cheng, Peng Wang, Ruigang Yang

Research output: Contribution to journalArticlepeer-review

124 Scopus citations


In this paper, we propose the convolutional spatial propagation network (CSPN) and demonstrate its effectiveness for various depth estimation tasks. CSPN is a simple and efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operations, in which the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). Compare to the previous state-of-the-art (SOTA) linear propagation model, i.e., spatial propagation networks (SPN), CSPN is 2 to 5× faster in practice. We concatenate CSPN and its variants to SOTA depth estimation networks, which significantly improve the depth accuracy. Specifically, we apply CSPN to two depth estimation problems: depth completion and stereo matching, in which we design modules which adapts the original 2D CSPN to embed sparse depth samples during the propagation, operate with 3D convolution and be synergistic with spatial pyramid pooling. In our experiments, we show that all these modules contribute to the final performance. For the task of depth completion, our method reduce the depth error over 30 percent in the NYU v2 and KITTI datasets. For the task of stereo matching, our method currently ranks 1st on both the KITTI Stereo 2012 and 2015 benchmarks.

Original languageEnglish
Article number8869936
Pages (from-to)2361-2379
Number of pages19
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number10
StatePublished - Oct 1 2020

Bibliographical note

Funding Information:
This work is supported by Baidu Inc.

Publisher Copyright:
© 1979-2012 IEEE.


  • Spatial propagation networks
  • depth completion
  • spatial pyramid pooling
  • stereo matching

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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