Stereo matching is one of the longest-standing problems in computer vision with close to 40
years of studies and research. Throughout the years the paradigm has shifted from local …

Estimating depth from single RGB images and videos is of widespread interest due to its
applications in many areas, including autonomous driving, 3D reconstruction, digital …

Extracting 3D information from a single optical image is very attractive. Recently emerging
self-supervised methods can learn depth representations without using ground truth depth …

Monocular depth estimation has become one of the most studied topics in computer vision.
Most approaches treat depth prediction as a fully supervised regression problem requiring …

Existing monocular depth estimation methods are unsatisfactory due to the inaccurate
inference of depth details and the loss of spatial information. In this paper, we present a …

Existing unsupervised monocular depth estimation methods resort to stereo image pairs
instead of ground-truth depth maps as supervision to predict scene depth. Constrained by …

Multi-scale representations deeply learned via convolutional neural networks have shown
tremendous importance for various pixel-level prediction problems. In this paper we present …

Many computer vision tasks, such as monocular depth estimation and height estimation from
a satellite orthophoto, have a common underlying goal, which is regression of dense …

Detecting objects and providing their 2D information (eg, size and center) are crucial for
outdoor visual surveillance. Because the cameras are static and their distances to …