Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending
and drawing extensive attention both from industry and academia. Conventional …

Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Abstract 3D scene understanding plays a vital role in vision-based autonomous driving.
While most existing methods focus on 3D object detection, they have difficulty describing …

In this research, we propose a new 3D object detector with a trustworthy depth estimation,
dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work …

Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with …

Abstract 3D visual perception tasks, including 3D detection and map segmentation based on
multi-camera images, are essential for autonomous driving systems. In this work, we present …

In this paper, we develop position embedding transformation (PETR) for multi-view 3D
object detection. PETR encodes the position information of 3D coordinates into image …

In this paper, we propose PETRv2, a unified framework for 3D perception from multi-view
images. Based on PETR, PETRv2 explores the effectiveness of temporal modeling, which …

Fusing the camera and LiDAR information has become a de-facto standard for 3D object
detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to …

Autonomous driving perceives its surroundings for decision making, which is one of the most
complex scenarios in visual perception. The success of paradigm innovation in solving the …