MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time

TitleMoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
Publication TypeConference Proceedings
Year of Publication2020
AuthorsZhou, X, Peng, Y, Long, C, Ren, F, Shi, C
Conference NameThe Thirty-seventh International Conference on Machine Learning
Date Published07/2020
Conference LocationVirtual Event
Keywords (or New Research Field)psclab

Monocular multi-object detection and localization in 3D space has been proven to be a challenging task. The MoNet3D algorithm is a novel and effective framework that can predict the 3D position of each object in a monocular image and draw a 3D bounding box for each object. The MoNet3D method incorporates prior knowledge of the spatial geometric correlation of neighboring objects into the deep neural network training process to improve the accuracy of 3D object localization. Experiments on the KITTI dataset show that the accuracy for predicting the depth and horizontal coordinates of objects in 3D space can reach 96.25% and 94.74%, respectively. Moreover, the method can realize the real-time image processing at 27.85 FPS, showing promising potential for embedded advanced driving assistance system applications. Our code is publicly available at

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