This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the bird's eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 14.9% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods.

该论文研究了自动驾驶场景下高精度的3D物体检测问题。其提出了Multi-View 3D networks（MV3D）框架，该框架采用多传感器融合技术，将LIDAR点云和RGB图像作为输入，并预测有方向的3D界限框。实验表明，该方法在3D定位和3D检测任务方面的表现优于现有技术约25％和30％，在2D检测中也表现出显著的技术优势。

用于自动驾驶的多视角三维物体检测网络