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3D Bounding Box Estimation Using Deep Learning and Geometry

A Tensorflow implementation of the paper: Mousavian, Arsalan, et al. 3D Bounding Box Estimation Using Deep Learning and Geometry by Fu-Hsiang Chan.

The aim of this project is to predict the size of the bounding box and orientation of the object in 3D space from a single two dimensional image.

Clone the repository

git clone https://github.com/smallcorgi/3D-Deepbox.git

Download the KITTI object detection dataset, calib and label (http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d).

Download the weights file (vgg_16.ckpt).

cd $3D-Deepbox_ROOT
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz
tar zxvf vgg_16_2016_08_28.tar.gz

Compile evaluation code

g++ -O3 -DNDEBUG -o ./kitti_eval/evaluate_object_3d_offline ./kitti_eval/evaluate_object_3d_offline.cpp

python main.py --mode train --gpu [gpu_id] --image [train_image_path] --label [train_label_path] --box2d [train_2d_boxes]

python main.py --mode test --gpu [gpu_id] --image [test_image_path] --box2d [test_2d_boxes_path] --model [model_path] --output [output_file_path]

./kitti_eval/evaluate_object_3d_offline [ground_truth_path] [predict_path]

car_detection AP: 100.000000 100.000000 100.000000

car_orientation AP: 98.482552 95.809959 91.975380

pedestrian_detection AP: 100.000000 100.000000 100.000000

pedestrian_orientation AP: 76.835083 74.943863 71.997620

cyclist_detection AP: 100.000000 100.000000 100.000000

cyclist_orientation AP: 89.908524 81.029915 79.436340

car_detection_ground AP: 90.743927 85.268692 76.673523

pedestrian_detection_ground AP: 97.148033 98.034355 98.376617

cyclist_detection_ground AP: 82.906242 82.897720 75.573006

Eval 3D bounding boxes

car_detection_3d AP: 84.500374 84.358612 75.764938

pedestrian_detection_3d AP: 96.662766 97.702209 89.280357

cyclist_detection_3d AP: 80.711548 81.337944 74.269547

mv "output_file" ./validation/result_2
cd ./3D-Deepbox/visualization
run run_demo.m