This project implements the paper 3D Bounding Box Estimation Using Deep Learning and Geometry, using a MultiBin approach to jointly estimate 3D box dimensions and orientation from monocular images. It uses a VGG16-based architecture combined with geometric constraints to regress 3D bounding boxes for cars, pedestrians, and cyclists. The model is trained and evaluated on the KITTI autonomous driving dataset.