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 …

We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical
flow network RAFT [35]. We introduce multi-level convolutional GRUs, which more efficiently …

While radar and video data can be readily fused at the detection level, fusing them at the
pixel level is potentially more beneficial. This is also more challenging in part due to the …

RGB-D data is essential for solving many problems in computer vision. Hundreds of public
RGB-D datasets containing various scenes, such as indoor, outdoor, aerial, driving, and …

Depth completion recovers a dense depth map from sensor measurements. Current
methods are mostly tailored for very sparse depth measurements from LiDARs in outdoor …

Stereo matching plays an essential role in various computer vision applications. Cost
volume is the crucial part in disparity estimation for measuring the similarity between the left …

In the last decades, researchers, practitioners and companies struggled in devising
mechanisms to detect malicious activities originating security threats. Amongst the many …

Robust monocular depth estimation (MDE) aims at learning a unified model that works
across diverse real-world scenes, which is an important and active topic in computer vision …

Real-time, robust, and accurate stereo depth-prediction algorithms deliver cutting-edge
performance in applications ranging from autonomous driving to augmented reality. Many …

Effectively measuring and modeling the reliability of a trained model is essential to the real-
world deployment of monocular depth estimation (MDE) models. However, the intrinsic ill …