TempoCross: instance-aware sparse representation for multimodal temporal fusion in 3D detection.

Authors

Changqiang Wang, Yuanqi Liang, Bolin Zhang, Xin Sui, Jiaxin Gao, Song Gao

Published in

Scientific reports. Jul 18, 2026. Epub Jul 18, 2026.

Abstract

With rising performance demands for autonomous driving perception systems, bird's-eye-view (BEV) perception that combines heterogeneous sensor data and temporal information has become a key research focus. Recent studies have advanced either multimodal temporal detection through BEV-level feature aggregation or sparse object-centric temporal modeling. However, integrating these two directions while preserving modality-specific temporal states remains less explored for LiDAR-camera detection. In this work, we present TempoCross, a 3D detection method based on instance-aware sparse representations for multimodal temporal fusion. TempoCross encodes features from different timestamps and sensor modalities in a unified instance space, enabling adaptive extraction of critical target information from modality- and time-specific states. Initially, both branches perform a preliminary cross-modal fusion to generate queries for the current frame. In the motion compensation module, a hybrid motion modeling strategy reduces alignment discrepancies between historical and current instances caused by complex object motion. This strategy combines explicit rigid-body transformations with implicit learnable deformation residuals, improving both accuracy and robustness in cross-frame instance association. Next, temporal-aware enhancement integrates the initial queries and current-frame features with motion-compensated historical instances. Finally, a lightweight cross-attention fuses current and historical instances from both branches. This formulation reduces the reliance on repeatedly propagating full-scene fused BEV features and concentrates temporal interaction on target-related instance states. On the nuScenes test set, TempoCross achieves 74.1% mAP and 75.7% NDS, outperforming mainstream baseline detectors. The results support the effectiveness of combining LiDAR-camera fusion with instance-aware sparse temporal modeling.

PMID:
42471444
Bibliographic data and abstract were imported from PubMed on 19 Jul 2026.

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