# 3D-Deepbox

**Repository Path**: huanglaoye/3D-Deepbox

## Basic Information

- **Project Name**: 3D-Deepbox
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-01-14
- **Last Updated**: 2020-12-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://arxiv.org/abs/1612.00496) 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.
 
## Prerequisites
1. TensorFlow
2. Numpy
3. OpenCV
4. tqdm

## Installation
1. Clone the repository
   ```Shell
   git clone https://github.com/smallcorgi/3D-Deepbox.git
   ```
2. Download the KITTI object detection dataset, calib and label (http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d).
3. Download the weights file (vgg_16.ckpt).
   ```Shell
   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
   ```
4. Compile evaluation code 
   ```Shell
   g++ -O3 -DNDEBUG -o ./kitti_eval/evaluate_object_3d_offline ./kitti_eval/evaluate_object_3d_offline.cpp
   ```
5. [KITTI train/val split used in 3DOP/Mono3D/MV3D](https://xiaozhichen.github.io/files/mv3d/imagesets.tar.gz)

## Usage

### Train model
   ```Shell
   python main.py --mode train --gpu [gpu_id] --image [train_image_path] --label [train_label_path] --box2d [train_2d_boxes]
   ```

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

### Evaluation on kitti
   ```Shell
   ./kitti_eval/evaluate_object_3d_offline [ground_truth_path] [predict_path]
   ```

### Result

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



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

## References
1. https://github.com/shashwat14/Multibin
2. https://github.com/experiencor/didi-starter/tree/master/simple_solution
3. https://github.com/experiencor/image-to-3d-bbox
4. https://github.com/prclibo/kitti_eval