3D Object Detection based on Multi-View Feature Point Matching

The result of object detection based on deep learning may have errors or omissions due to the occlusion and background in object detection, which is an intractable problem. An effective method of improving object detection performance using multiple viewpoint images are proposed. By performing feature point matching on objects in the overlap between different views, groups of points with semantic information can be obtained. These point groups can be used to generate new detection boxes, which can correct error ones in the raw results. Experiments show that the proposed method is a viable solution, the recall is significantly improved.