• Corpus ID: 211010598

Depth Map Estimation of Dynamic Scenes Using Prior Depth Information

@article{Noraky2020DepthME,
  title={Depth Map Estimation of Dynamic Scenes Using Prior Depth Information},
  author={James Noraky and Vivienne Sze},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.00297}
}
Depth information is useful for many applications. Active depth sensors are appealing because they obtain dense and accurate depth maps. However, due to issues that range from power constraints to multi-sensor interference, these sensors cannot always be continuously used. To overcome this limitation, we propose an algorithm that estimates depth maps using concurrently collected images and a previously measured depth map for dynamic scenes, where both the camera and objects in the scene may be… 
2 Citations

References

SHOWING 1-10 OF 36 REFERENCES
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation
TLDR
This work shows that, given per-pixel optical flow correspondences between two consecutive frames and, the sparse depth prior for the reference frame, it can effectively recover the dense depth map for the successive frames without solving for 3D motion parameters.
Dense Monocular Depth Estimation in Complex Dynamic Scenes
TLDR
A novel motion segmentation algorithm is provided that segments the optical flow field into a set of motion models, each with its own epipolar geometry, and it is shown that the scene can be reconstructed based on these motion models by optimizing a convex program.
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling
TLDR
The technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and is demonstrated through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.
Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging
  • J. Noraky, V. Sze
  • Computer Science
    IEEE Transactions on Circuits and Systems for Video Technology
  • 2020
TLDR
This work presents an algorithm that lowers the power for depth sensing by reducing the usage of the TOF camera and estimating depth maps using concurrently collected images, and designs it to run on a low power embedded platform.
Dense multibody motion estimation and reconstruction from a handheld camera
TLDR
A dense solution to all three elements of this problem: depth estimation, motion label assignment and rigid transformation estimation directly from the raw video by optimizing a single cost function using a hill-climbing approach.
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
TLDR
This work addresses unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, as cameras are the cheapest, least restrictive and most ubiquitous sensor for robotics.
Learning the Depths of Moving People by Watching Frozen People
TLDR
This paper takes a data-driven approach and learns human depth priors from a new source of data: thousands of Internet videos of people imitating mannequins, i.e., freezing in diverse, natural poses, while a hand-held camera tours the scene.
Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras
TLDR
This work is the first to learn the camera intrinsic parameters, including lens distortion, from video in an unsupervised manner, thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale.
Texture aided depth frame interpolation
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