Co-Fusion4D：面向鲁棒3D目标检测的时空协同融合
Co-Fusion4D: Spatio-temporal Collaborative Fusion for Robust 3D Object Detection
AI总结：
提出Co-Fusion4D框架，通过当前帧主导-历史帧互补机制和双注意力融合模块，解决BEV检测器中跨帧时空不一致问题，在nuScenes上达到74.9% mAP和75.6% NDS。
摘要：
在自动驾驶中，3D目标检测对于准确感知和可靠决策至关重要。然而，目标运动和自车运动常常在基于BEV的检测器中引起跨帧时空不一致，导致时序BEV特征错位和时空一致性退化。为了解决这些挑战，我们提出了Co-Fusion4D，一个统一框架，显式地保持跨帧时空一致性并抑制时序特征漂移。Co-Fusion4D采用当前帧中心策略，将当前帧作为主要信息源，同时在时空滤波和对齐后选择性地融入历史帧。这种主从互补机制有效减轻了累积对齐误差，抑制了噪声特征传播，并利用可靠的时序线索获得更一致的BEV表示。此外，Co-Fusion4D集成了双注意力融合（DAF）模块，以进一步增强时空特征交互。DAF联合利用帧内空间注意力和帧间时序注意力，自适应地对齐和融合多帧特征，强调运动一致区域同时抑制虚假相关性。通过偏离传统的均匀融合范式，该设计显著提高了BEV表示的时序稳定性和判别能力。在nuScenes基准上的大量实验表明，Co-Fusion4D实现了最先进的性能，mAP为74.9%，NDS为75.6%，且不依赖测试时增强或外部数据。
英文摘要：
In autonomous driving, 3D object detection is essential for accurate perception and reliable decision-making. However, object motion and ego-motion often induce cross-frame spatiotemporal inconsistencies in BEV-based detectors, leading to temporal BEV feature misalignment and degraded spatiotemporal consistency. To address these challenges, we propose Co-Fusion4D, a unified framework that explicitly preserves cross-frame spatiotemporal consistency and suppresses temporal feature drift. Co-Fusion4D adopts a current-frame-centric strategy, treating the current frame as the primary source of information while selectively incorporating historical frames after spatiotemporal filtering and alignment. This dominant-complementary mechanism effectively mitigates cumulative alignment errors, suppresses noisy feature propagation, and exploits reliable temporal cues for a more consistent BEV representation. In addition, Co-Fusion4D integrates a Dual Attention Fusion (DAF) module to further enhance spatiotemporal feature interaction. DAF jointly leverages intra-frame spatial attention and inter-frame temporal attention to adaptively align and fuse multi-frame features, emphasizing motion-consistent regions while suppressing spurious correlations. By departing from conventional uniform fusion paradigms, this design substantially improves the temporal stability and discriminative capability of BEV representations. Extensive experiments on the nuScenes benchmark demonstrate that Co-Fusion4D achieves state-of-the-art performance, with 74.9% mAP and 75.6% NDS, without relying on test-time augmentation or external data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
