In recent years, numerous real-time stereo matching methods have been introduced, but
they often lack accuracy. These methods attempt to improve accuracy by introducing new …

Despite the remarkable progress facilitated by learning-based stereo-matching algorithms,
the performance in the ill-conditioned regions, such as the occluded regions, remains a …

Stereo matching is close to hitting a half-century of history, yet witnessed a rapid evolution in
the last decade thanks to deep learning. While previous surveys in the late 2010s covered …

Deep neural networks have consistently represented the state of the art in most computer
vision problems. In these scenarios, larger and more complex models have demonstrated …

Abstract We introduce the Double Cost Volume Stereo Matching Network (DCVSMNet 1), a
novel architecture characterized by two upper (group-wise correlation) and lower (norm …

Stereo matching has become a key technique for 3D environment perception in intelligent
vehicles. For a considerable time, convolutional neural networks (CNNs) have remained the …

Multi-fisheye System has the advantages of sufficient overlap and the ability to capture a
complete 360° scene, which is beneficial for the omnidirectional depth estimation task …

In stereo vision, self-similar or bland regions can make it difficult to match patches between
two images. Active stereo-based methods mitigate this problem by projecting a pseudo …

We propose a lightweight system for stereo-matching using embedded graphic processing
units (GPUs). The proposed system overcomes the trade-off between accuracy and …

Efficient and accurate depth estimation is crucial for real-world embedded vision
applications, such as autonomous driving, 3D reconstruction, and drone navigation. Stereo …