  Monocular Depth Estimation (MDE) aims to predict pixel-wise depth given a
single RGB image. For both, the convolutional as well as the recent
attention-based models, encoder-decoder-based architectures have been found to
be useful due to the simultaneous requirement of global context and pixel-level
resolution. Typically, a skip connection module is used to fuse the encoder and
decoder features, which comprises of feature map concatenation followed by a
convolution operation. Inspired by the demonstrated benefits of attention in a
multitude of computer vision problems, we propose an attention-based fusion of
encoder and decoder features. We pose MDE as a pixel query refinement problem,
where coarsest-level encoder features are used to initialize pixel-level
queries, which are then refined to higher resolutions by the proposed Skip
Attention Module (SAM). We formulate the prediction problem as ordinal
regression over the bin centers that discretize the continuous depth range and
introduce a Bin Center Predictor (BCP) module that predicts bins at the
coarsest level using pixel queries. Apart from the benefit of image adaptive
depth binning, the proposed design helps learn improved depth embedding in
initial pixel queries via direct supervision from the ground truth. Extensive
experiments on the two canonical datasets, NYUV2 and KITTI, show that our
architecture outperforms the state-of-the-art by 5.3% and 3.9%, respectively,
along with an improved generalization performance by 9.4% on the SUNRGBD
dataset. Code is available at https://github.com/ashutosh1807/PixelFormer.git.
