  We propose a unified object-aware temporal learning framework for multi-view
3D detection and tracking tasks. Having observed that the efficacy of the
temporal fusion strategy in recent multi-view perception methods may be
weakened by distractors and background clutters in historical frames, we
propose a cyclic learning mechanism to improve the robustness of multi-view
representation learning. The essence is constructing a backward bridge to
propagate information from model predictions (e.g., object locations and sizes)
to image and BEV features, which forms a circle with regular inference. After
backward refinement, the responses of target-irrelevant regions in historical
frames would be suppressed, decreasing the risk of polluting future frames and
improving the object awareness ability of temporal fusion. We further tailor an
object-aware association strategy for tracking based on the cyclic learning
model. The cyclic learning model not only provides refined features, but also
delivers finer clues (e.g., scale level) for tracklet association. The proposed
cycle learning method and association module together contribute a novel and
unified multi-task framework. Experiments on nuScenes show that the proposed
model achieves consistent performance gains over baselines of different designs
(i.e., dense query-based BEVFormer, sparse query-based SparseBEV and LSS-based
BEVDet4D) on both detection and tracking evaluation.
