Neural RGB→D Sensing: Depth and Uncertainty from a Video Camera

Neural RGB→D Sensing: Depth and Uncertainty from a Video Camera (CVPR 2019)

Authors: Chao Liu, Jinwei Gu, Kihwan Kim, Srinivasa G. Narasimhan, Jan Kautz
Affiliations: NVIDIA, Carnegie Mellon University, SenseTime

• CVF Mirror Arxiv mirror
• Supplemental (zip)

The main ideas here are:

• Estimate a "depth probability distribution" rather than a single value
  • For each image, we get a "Depth Probability Volume (DPV)" representing a depth MLE and an uncertainty measure.
• Accumulate DPV estimates across time or across frames.

Method

• They first estimate the DPV using a network called the D-Net.
• Next they calculate the difference between the current predicted DPV and the previous DPV.

  This residual is passed through the K-Net to create an update.

  The update is added to the previous DPV to create the updated DPV.

• An R-Net refines the updated DPV using input image features from the D-Net.

Architecture

See their supplementary material for details.

D-Net

The D-Net consists of 28 convolutional blocks followed by 4 branches of spatial pyramid layers and a fusion layer.