Adaptive Discrete Disparity Volume for Self-supervised Monocular Depth
  Estimation
Jianwei Ren
In self-supervised monocular depth estimation tasks, discrete disparity
prediction has been proven to attain higher quality depth maps than common
continuous methods. However, current discretization strategies often divide
depth ranges of scenes into bins in a handcrafted and rigid manner, limiting
model performance. In this paper, we propose a learnable module, Adaptive
Discrete Disparity Volume (ADDV), which is capable of dynamically sensing depth
distributions in different RGB images and generating adaptive bins for them.
Without any extra supervision, this module can be integrated into existing CNN
architectures, allowing networks to produce representative values for bins and
a probability volume over them. Furthermore, we introduce novel training
strategies - uniformizing and sharpening - through a loss term and temperature
parameter, respectively, to provide regularizations under self-supervised
conditions, preventing model degradation or collapse. Empirical results
demonstrate that ADDV effectively processes global information, generating
appropriate bins for various scenes and producing higher quality depth maps
compared to handcrafted methods.