Dense Monocular Depth Estimation in Complex Dynamic Scenes

@article{Ranftl2016DenseMD,
  title={Dense Monocular Depth Estimation in Complex Dynamic Scenes},
  author={Rene Ranftl and Vibhav Vineet and Qifeng Chen and Vladlen Koltun},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={4058-4066}
}
We present an approach to dense depth estimation from a single monocular camera that is moving through a dynamic scene. The approach produces a dense depth map from two consecutive frames. Moving objects are reconstructed along with the surrounding environment. We provide a novel motion segmentation algorithm that segments the optical flow field into a set of motion models, each with its own epipolar geometry. We then show that the scene can be reconstructed based on these motion models by… CONTINUE READING
Highly Cited
This paper has 38 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 24 extracted citations

Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames

2017 IEEE International Conference on Computer Vision (ICCV) • 2017
View 7 Excerpts
Highly Influenced

Direct Sparse Odometry

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2018
View 1 Excerpt

Mono-Stixels: Monocular Depth Reconstruction of Dynamic Street Scenes

2018 IEEE International Conference on Robotics and Automation (ICRA) • 2018
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 40 references

Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2016
View 1 Excerpt

Efficient sparse-to-dense optical flow estimation using a learned basis and layers

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2015
View 1 Excerpt

Intrinsic Depth: Improving Depth Transfer with Intrinsic Images

2015 IEEE International Conference on Computer Vision (ICCV) • 2015
View 1 Excerpt

Joint SFM and detection cues for monocular 3D localization in road scenes

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2015
View 1 Excerpt

Similar Papers

Loading similar papers…