Surround-view fisheye camera perception for automated driving: Overview, survey & challenges
Surround-view fisheye cameras are commonly used for near-field sensing in automated
driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360° around …
driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360° around …
Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance,
augmented reality and in particular automotive applications. In spite of their prevalence …
augmented reality and in particular automotive applications. In spite of their prevalence …
SynWoodScape: Synthetic surround-view fisheye camera dataset for autonomous driving
Surround-view cameras are a primary sensor for automated driving, used for near-field
perception. It is one of the most commonly used sensors in commercial vehicles primarily …
perception. It is one of the most commonly used sensors in commercial vehicles primarily …
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation
Abstract Bird's-eye-view (BEV) grid is a common representation for the perception of road
components, eg, drivable area, in autonomous driving. Most existing approaches rely on …
components, eg, drivable area, in autonomous driving. Most existing approaches rely on …
Near-field perception for low-speed vehicle automation using surround-view fisheye cameras
Cameras are the primary sensor in automated driving systems. They provide high
information density and are optimal for detecting road infrastructure cues laid out for human …
information density and are optimal for detecting road infrastructure cues laid out for human …
Optical flow for autonomous driving: Applications, challenges and improvements
Optical flow estimation is a well-studied topic for automated driving applications. Many
outstanding optical flow estimation methods have been proposed, but they become …
outstanding optical flow estimation methods have been proposed, but they become …
Real-time joint object detection and semantic segmentation network for automated driving
G Sistu, I Leang, S Yogamani - arXiv preprint arXiv:1901.03912, 2019 - arxiv.org
Convolutional Neural Networks (CNN) are successfully used for various visual perception
tasks including bounding box object detection, semantic segmentation, optical flow, depth …
tasks including bounding box object detection, semantic segmentation, optical flow, depth …
Monocular fisheye camera depth estimation using sparse lidar supervision
Near-field depth estimation around a self-driving car is an important function that can be
achieved by four wide-angle fisheye cameras having a field of view of over 180°. Depth …
achieved by four wide-angle fisheye cameras having a field of view of over 180°. Depth …
Adversarial attacks on multi-task visual perception for autonomous driving
Deep neural networks (DNNs) have accomplished impressive success in various
applications, including autonomous driving perception tasks, in recent years. On the other …
applications, including autonomous driving perception tasks, in recent years. On the other …
Limoseg: Real-time bird's eye view based lidar motion segmentation
Moving object detection and segmentation is an essential task in the Autonomous Driving
pipeline. Detecting and isolating static and moving components of a vehicle's surroundings …
pipeline. Detecting and isolating static and moving components of a vehicle's surroundings …