Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

Monocular depth estimation is vital for scene understanding and downstream tasks. We
focus on the supervised setup, in which ground-truth depth is available only at training time …

Estimating accurate depth from a single image is challenging because it is an ill-posed
problem as infinitely many 3D scenes can be projected to the same 2D scene. However …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

While convolutional neural networks have shown a tremendous impact on various computer
vision tasks, they generally demonstrate limitations in explicitly modeling long-range …

We propose a monocular depth estimation method SC-Depth, which requires only
unlabelled videos for training and enables the scale-consistent prediction at inference time …

Monocular depth prediction plays a crucial role in understanding 3D scene geometry.
Although recent methods have achieved impressive progress in evaluation metrics such as …

Video frame interpolation aims to synthesize nonexistent frames in-between the original
frames. While significant advances have been made from the recent deep convolutional …

Monocular depth estimation, which plays a crucial role in understanding 3D scene
geometry, is an ill-posed prob-lem. Recent methods have gained significant improvement by …