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 completion aims at predicting dense pixel-wise depth from an extremely sparse map
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …

The video-language (VL) pretraining has achieved remarkable improvement in multiple
downstream tasks. However, the current VL pretraining framework is hard to extend to …

Monocular depth estimation is fundamental for 3D scene understanding and downstream
applications. However, even under the supervised setup, it is still challenging and ill-posed …

Denoising diffusion probabilistic models have transformed image generation with their
impressive fidelity and diversity. We show that they also excel in estimating optical flow and …

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 …

Monocular depth estimation (MDE) is a fundamental topic of geometric computer vision and
a core technique for many downstream applications. Recently, several methods reframe the …

Monocular depth estimation has drawn widespread attention from the vision community due
to its broad applications. In this paper, we propose a novel physics (geometry)-driven deep …

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 …

In this paper, we investigate the issue of knowledge distillation for training compact semantic
segmentation networks by making use of cumbersome networks. We start from the …