Object 3D position estimation based on instance segmentation

The difficulty of 3D target detection in practical engineering applications lies in the high price of depth perception equipment, poor point cloud quality, lack of rich texture information, difficulty in creating 3D data training sets. This paper proposes a three-dimensional target position estimation method based on instance segmentation. It can be used in a variety of sensors, such as camera-radar, binocular camera, etc. Firstly, the target segmentation is performed under the two-dimensional image, the target's depth image are extracted and RGB image according to the target segmentation mask, and it is converted into a rough point cloud. Finally, the abnormal noise points is removed to obtain a fine target point cloud. Tested on the KITTI data set, the AP can reach 50%. The results show that this method can accurately estimate the target location information. The method proposed in this paper does not need 3D data training set can quickly and accurately extract the point cloud of three-dimensional objects, and only use a two-dimensional detector to achieve the purpose of three-dimensional object detection.