We present PointFusion, a generic 3D object detection method that leverages both image and 3D point cloud information. Unlike existing methods that either use multi-stage pipelines or hold sensor and dataset-specific assumptions, PointFusion is conceptually simple and application-agnostic. The image data and the raw point cloud data are independently processed by a CNN and a PointNet architecture, respectively. The resulting outputs are then combined by a novel fusion network, which predicts multiple 3D box hypotheses and their confidences, using the input 3D points as spatial anchors. We evaluate PointFusion on two distinctive datasets: the KITTI dataset that features driving scenes captured with a lidar-camera setup, and the SUN-RGBD dataset that captures indoor environments with RGB-D cameras. Our model is the first one that is able to perform better or on-par with the state-of-the-art on these diverse datasets without any dataset-specific model tuning.

使用PointFusion实现基于图像和点云信息的3D对象检测方法，其中CNN和PointNet网络分别处理图像和点云数据，再由新型融合网络将二者输出结合起来预测多个3D框及其置信度，相比现有方法在KITTI和SUN-RGBD数据集上实现更好的检测结果且无需特定模型调整。

PointFusion：用于3D边界框估计的深度传感器融合