We consider the problem of Bird's Eye View (BEV) segmentation with perspective monocular
camera view as input. An effective solution to this problem is important in many autonomous …

Semantic segmentation is an effective way to perform scene understanding. Recently,
segmentation in 3D Bird's Eye View (BEV) space has become popular as its directly used by …

Generating a detailed near-field perceptual model of the environment is an important and
challenging problem in both self-driving vehicles and autonomous mobile robotics. A Bird's …

Semantic segmentation is an effective way to perform scene understanding. Recently
segmentation in 3D Bird's Eye View (BEV) space has become popular as its directly used by …

Autonomous vehicles (AV) require that neural networks used for perception be robust to
different viewpoints if they are to be deployed across many types of vehicles without the …

Visual bird's eye view (BEV) semantic segmentation helps autonomous vehicles understand
the surrounding environment only from images, including static elements (eg, roads) and …

Recent works in autonomous driving have widely adopted the bird'seye-view (BEV)
semantic map as an intermediate representation of the world. Online prediction of these BEV …

Semantic segmentation in bird's eye view (BEV) plays a crucial role in autonomous driving.
Previous methods usually follow an end-to-end pipeline directly predicting the BEV …

Bird's-eye-view (BEV) is a powerful and widely adopted representation for road scenes that
captures surrounding objects and their spatial locations, along with overall context in the …

Bird's-Eye-View (BEV) maps have emerged as one of the most powerful representations for
scene understanding due to their ability to provide rich spatial context while being easy to …