Vertex points are not enough: Monocular 3D object detection via intra- and inter-plane constraints.

Existed methods for 3D object detection in monocular images focus mainly on the class of rigid bodies like cars, while more challenging detection like the cyclist is less studied. Therefore, we propose a novel 3D monocular object detection method to improve the accuracy of detection objects with large differences in deformation by introducing the geometric constraints of the object 3D bounding box plane. Considering the map relationship of projection plane and the keypoint, we firstly introduce the geometric constraints of the object 3D bounding box plane, adding the intra-plane constraint while regressing the position and offset of the keypoint itself, so that the position and offset error of the keypoint are always within the error range of the projection plane. For the inter-plane geometry relationship of the 3D bounding box, the prior knowledge is incorporated to optimize the keypoint regression allowing for improved the accuracy of depth location prediction. Experimental results show that the proposed method outperforms some other state-of-the-art methods on cyclist class, and obtains competitive results in the field of real-time monocular detection.