In the realm of autonomous driving, LiDAR and camera sensors play an indispensable role, furnishing pivotal observational data for the critical task of precise 3D object detection. Existing fusion algorithms effectively utilize the complementary data from both sensors. However, these methods typically concatenate the raw point cloud data and pixel-level image features, unfortunately, a process that introduces errors and results in the loss of critical information embedded in each modality. To mitigate the problem of lost feature information, this paper proposes a Cross-Attention Dynamic Fusion (CADF) strategy that dynamically fuses the two heterogeneous data sources. In addition, we acknowledge the issue of insufficient data augmentation for these two diverse modalities. To combat this, we propose a Synchronous Data Augmentation (SDA) strategy designed to enhance training efficiency. We have tested our method using the KITTI and nuScenes datasets, and the results have been promising. Remarkably, our top-performing model attained an 82.52% mAP on the KITTI test benchmark, outperforming other state-of-the-art methods.

中文翻译：

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DyFusion：具有动态融合的交叉注意力 3D 对象检测

在自动驾驶领域，LiDAR 和摄像头传感器发挥着不可或缺的作用，为精确 3D 物体检测的关键任务提供关键的观测数据。现有的融合算法有效地利用了来自两个传感器的互补数据。然而，不幸的是，这些方法通常将原始点云数据和像素级图像特征连接起来，这个过程会引入错误并导致嵌入在每种模态中的关键信息丢失。为了缓解特征信息丢失的问题，本文提出了一种交叉注意动态融合（CADF）策略，动态融合两个异构数据源。此外，我们承认这两种不同模式的数据增强不足的问题。为了解决这个问题，我们提出了一种同步数据增强（SDA）策略，旨在提高训练效率。我们使用 KITTI 和 nuScenes 数据集测试了我们的方法，结果令人鼓舞。值得注意的是，我们表现最好的模型在 KITTI 测试基准上获得了 82.52% 的 mAP，优于其他最先进的方法。