BEVHeight++: Toward Robust Visual Centric 3D Object Detection
Lei Yang Tao Tang Jun Li Peng Chen Kun Yuan Li Wang Yi Huang Xinyu Zhang Kaicheng Yu
While most recent autonomous driving system focuses on developing perception
methods on ego-vehicle sensors, people tend to overlook an alternative approach
to leverage intelligent roadside cameras to extend the perception ability
beyond the visual range. We discover that the state-of-the-art vision-centric
bird's eye view detection methods have inferior performances on roadside
cameras. This is because these methods mainly focus on recovering the depth
regarding the camera center, where the depth difference between the car and the
ground quickly shrinks while the distance increases. In this paper, we propose
a simple yet effective approach, dubbed BEVHeight++, to address this issue. In
essence, we regress the height to the ground to achieve a distance-agnostic
formulation to ease the optimization process of camera-only perception methods.
By incorporating both height and depth encoding techniques, we achieve a more
accurate and robust projection from 2D to BEV spaces. On popular 3D detection
benchmarks of roadside cameras, our method surpasses all previous
vision-centric methods by a significant margin. In terms of the ego-vehicle
scenario, our BEVHeight++ possesses superior over depth-only methods.
Specifically, it yields a notable improvement of +1.9% NDS and +1.1% mAP over
BEVDepth when evaluated on the nuScenes validation set. Moreover, on the
nuScenes test set, our method achieves substantial advancements, with an
increase of +2.8% NDS and +1.7% mAP, respectively.