Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a
solution with simple configuration compared to typical multi-sensor systems. The main …

We approach instantaneous mapping, converting images to a top-down view of the world, as
a translation problem. We show how a novel form of transformer network can be used to …

Sensing surroundings plays a crucial role in human spatial perception, as it extracts the
spatial configuration of objects as well as the free space from the observations. To facilitate …

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited
due to the deployed expensive sensors and time-consuming computation. Camera-based …

Constructing Birds-Eye-View (BEV) maps from monocular images is typically a complex
multi-stage process involving the separate vision tasks of ground plane estimation, road …

A reliable, punctual, and spatially accurate dataset of sidewalks is vital for identifying where
improvements can be made upon urban environment to enhance multi-modal accessibility …

In this paper, we address the novel, highly challenging problem of estimating the layout of a
complex urban driving scenario. Given a single color image captured from a driving platform …

We introduce a novel representation called Ordered Atomic Activity for interactive scenario
understanding. The representation decomposes each scenario into a set of ordered atomic …

In this work, we propose PolarBEV for vision-based uneven BEV representation learning. To
adapt to the foreshortening effect of camera imaging, we rasterize the BEV space both …