论文标题
Imlidar：3D对象检测的跨传感器动态消息传播网络
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection
论文作者
论文摘要
LIDAR和相机作为两个不同的传感器，提供3D场景的几何（点云）和语义（RGB图像）信息。但是，现有方法融合两个交叉传感器的数据，使其与质量3D对象检测（3OD）互补，仍然具有挑战性。我们提出了Imlidar，这是一种新的3OD范式，通过逐步融合相机图像和LiDar Point Clouds的多尺度特征来缩小交叉传感器差异。 Imlidar使得可以为检测头提供交叉传感器但稳健融合的功能。为此，Imlidar中存在两个核心设计。首先，我们提出了一个跨传感器动态消息传播模块，以结合多尺度图像和点特征的最佳功能。其次，我们提出了一个直接的预测问题，该问题允许设计有效的基于设置的检测器，以解决分类和本地化信心的不一致以及手工调整的超参数的敏感性。此外，新型基于集合的检测器可以可拆卸并容易整合到各种检测网络中。对Kitti和Sun-RGBD数据集的比较都显示出与23个最先进的3OD方法相比，我们的Imlidar的视觉和数值改进。
LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3D scenes. However, it is still challenging for existing methods to fuse data from the two cross sensors, making them complementary for quality 3D object detection (3OD). We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds. ImLiDAR enables to provide the detection head with cross-sensor yet robustly fused features. To achieve this, two core designs exist in ImLiDAR. First, we propose a cross-sensor dynamic message propagation module to combine the best of the multi-scale image and point features. Second, we raise a direct set prediction problem that allows designing an effective set-based detector to tackle the inconsistency of the classification and localization confidences, and the sensitivity of hand-tuned hyperparameters. Besides, the novel set-based detector can be detachable and easily integrated into various detection networks. Comparisons on both the KITTI and SUN-RGBD datasets show clear visual and numerical improvements of our ImLiDAR over twenty-three state-of-the-art 3OD methods.
