preparation.md

Data Preparation

You need to download KITTI dataset here. Download left images, calibration files and labels. Download the split files here and place them at ${YOUR_KITTI_DIR}/SPLIT/ImageSets. Your data folder should look like this:

${YOUR_KITTI_DIR}
├── training
   ├── calib
       ├── xxxxxx.txt (Camera parameters for image xxxxxx)
   ├── image_2
       ├── xxxxxx.png (image xxxxxx)
   ├── label_2
       ├── xxxxxx.txt (object labels for image xxxxxx)
   ├── ImageSets
      ├── train.txt
      ├── val.txt   
      ├── trainval.txt        
├── testing
   ├── calib
       ├── xxxxxx.txt (Camera parameters for image xxxxxx)
   ├── image_2
       ├── xxxxxx.png (image xxxxxx)
   ├── ImageSets
      ├── test.txt

Download pre-trained model

You need to download the pre-trained checkpoints here in order to use Ego-Net. Unzip it to ${YOUR_MODEL_DIR}.

Compile the official evaluator

Go to the folder storing the source code

cd ${EgoNet_DIR}/tools/kitti-eval 

Compile the source code

g++ -o evaluate_object_3d_offline evaluate_object_3d_offline.cpp -O3

Download the input bounding boxes

Download the resources folder and unzip its contents. Place the resource folder at ${EgoNet_DIR}/resources

Environment

You need to create an environment that meets the following dependencies. The versions included in the parenthesis are tested. Other versions may also work but are not tested.

• Python (3.7.9)
• Numpy (1.19.2)
• PyTorch (1.6.0, GPU required)
• Scipy (1.5.2)
• Matplotlib (3.3.4)
• OpenCV (3.4.2)
• pyyaml (5.4.1)

For more details of my tested local environment, refer to spec-list.txt. The recommended environment manager is Anaconda, which can create an environment using this provided spec-list. For debugging using an IDE, I personally use and recommend Spyder 4.2 which you can get by

conda install spyder