Multi-view 3D object detection is becoming popular in autonomous driving due
to its high effectiveness and low cost. Most of the current state-of-the-art
detectors follow the query-based bird's-eye-view (BEV) paradigm, which benefits
from both BEV's strong perception power and end-to-end pipeline. Despite
achieving substantial progress, existing works model objects via globally
leveraging temporal and spatial information of BEV features, resulting in
problems when handling the challenging complex and dynamic autonomous driving
scenarios. In this paper, we proposed an Object-Centric query-BEV detector
OCBEV, which can carve the temporal and spatial cues of moving targets more
effectively. OCBEV comprises three designs: Object Aligned Temporal Fusion
aligns the BEV feature based on ego-motion and estimated current locations of
moving objects, leading to a precise instance-level feature fusion. Object
Focused Multi-View Sampling samples more 3D features from an adaptive local
height ranges of objects for each scene to enrich foreground information.
Object Informed Query Enhancement replaces part of pre-defined decoder queries
in common DETR-style decoders with positional features of objects on
high-confidence locations, introducing more direct object positional priors.
Extensive experimental evaluations are conducted on the challenging nuScenes
dataset. Our approach achieves a state-of-the-art result, surpassing the
traditional BEVFormer by 1.5 NDS points. Moreover, we have a faster convergence
speed and only need half of the training iterations to get comparable
performance, which further demonstrates its effectiveness.

提出了一种新的 Object-Centric query-BEV 检测器 OCBEV 方法，它可以更有效地针对动态场景中移动目标的时间和空间线索。经过在挑战性的 nuScenes 数据集进行广泛实验评估后，该方法取得了最先进的结果，比传统的 BEVFormer 提高了 1.5 个 NDS 点，并具有更快的收敛速度和更少的训练迭代次数来达到相似的性能。