基于空间约束的自适应单目3D物体检测算法

引入无须先验模版匹配的3D目标检测算法，通过简化消失点（VP）计算和改进角点提取等步骤，提出一种自适应的单目3D物体检测算法. 针对复杂场景下VP 计算易受干扰的问题，根据室内场景中世界坐标系、相机以及目标物体之间的空间关系，建立目标、相机偏航角与VP之间的约束模型，提出一种基于空间约束的 M 估计子抽样一致性（MSAC）消失点计算方法；为了提高3D框的估计精度，在VP透视关系的基础上，提出一种自适应估计3D框角点的方法，通过建立目标3D轮廓线与2D框的空间约束关系，实现目标物体的3D框快速检测. 相关数据集的实验结果表明，所提方法相比于其他算法不仅在室内场景下具有估计精度高、实时性好的优势，而且在室外场景实验下也具有更好的精度和鲁棒性.

Adaptive monocular 3D object detection algorithm based on spatial constraint

The 3D-Cube algorithm without prior template matching was introduced, and an algorithm for adaptive detection of 3D objects was proposed. Firstly, the relationship among the camera, the object and the VP vanishing point was established, according to the transformation relationship between the world coordinate system, the camera and the moving target. By combining the spatial constraint relationship, a space constrained M-estimator sample and consensus (MSAC) algorithm was proposed to improve the robustness in complex scenes. To improve the accuracy of 3D frame estimation, an adaptive method of 3D frame corner estimation was proposed based on the VP perspective relationship. The 3D bounding box of the target object could be detected quickly by building the spatial constraint relation between 3D contour and 2D frame of the target. The experimental results show that the proposed method has the advantages of high accuracy and real-time performance, compared with other algorithms in indoor scenes, which also has better accuracy and robustness in outdoor scene experiment.