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 …

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

Abstract This work presents Depth Anything a highly practical solution for robust monocular
depth estimation. Without pursuing novel technical modules we aim to build a simple yet …

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 …

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 …

We present a novel bird's-eye-view (BEV) detector with perspective supervision, which
converges faster and better suits modern image backbones. Existing state-of-the-art BEV …

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 work, we present a unified framework for multi-modality 3D object detection, named
UVTR. The proposed method aims to unify multi-modality representations in the voxel space …

We present cross-view transformers, an efficient attention-based model for map-view
semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping …

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …