This is an unofficial inplementation of VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection in TensorFlow. A large part of this project is based on the work here. Thanks to @jeasinema. This work is a modified version with bugs fixed and better experimental settings to chase the results reported in the paper (still ongoing).
python3.6+
TensorFlow 2.0
opencv
shapely
numba
easydict
-
Clone this repository.
-
Install the dependencies
pip3 install -r requirements.txt
- Compile the Cython module
python3 setup.py build_ext --inplace
- Compile the evaluation code
cd kitti_eval
g++ -std=c++14 -o evaluate_object_3d_offline evaluate_object_3d_offline.cpp
- Grant the execution permission to evaluation script
cd kitti_eval
chmod +x launch_test.sh
-
Download the 3D KITTI detection dataset from here. Data to download include:
- Velodyne point clouds (29 GB): input data to VoxelNet
- Training labels of object data set (5 MB): input label to VoxelNet
- Camera calibration matrices of object data set (16 MB): for visualization of predictions
- Left color images of object data set (12 GB): for visualization of predictions
-
After downloading the 4 folders mentioned above, we will use
data.crop.py
to get the cropped point cloud data for training and validation. Point clouds outside the image coordinates will be removed. To do so, update the directories onlines 82 - 85
indata/crop.py
and rundata/crop.py
to generate the cropped data. Note that cropped point cloud data will overwrite raw point cloud data.
cd data
python crop.py
- Split the training set into training and validation set according to the protocol here. And rearrange the folders to have the following structure:
└── cropped_dataset
├── training <-- training data
| ├── image_2
| ├── label_2
| └── velodyne
└── validation <--- evaluation data
| ├── image_2
| ├── label_2
| └── velodyne
- Update the dataset directory in
config.py
andkitti_eval/launch_test.sh
- Specify the GPUs to use in
config.py
- run
train.py
with desired hyper-parameters to start training:
$ python3 train.py --alpha 1 --beta 10
Note that the hyper-parameter settings introduced in the paper are not able to produce high quality results. So, a different setting is specified here.
Training on two Nvidia 1080 Ti GPUs takes around 3 days (160 epochs as reported in the paper). During training, training statistics are recorded in log/default
, which can be monitored by tensorboard. And models are saved in save_model/default
. Intermediate validation results will be dumped into the folder predictions/XXX/data
with XXX
as the epoch number. And metrics will be calculated and saved in predictions/XXX/log
. If the --vis
flag is set to be True
, visualizations of intermediate results will be dumped in the folder predictions/XXX/vis
.
- When the training is done, executing
parse_log.py
will generate the learning curve.
$ python3 parse_log.py predictions
- There is a pre-trained model for car in
save_model/pre_trained_car
.
- run
test.py -n default
to produce final predictions on the validation set after training is done. Change-n
flag topre_trained_car
will start testing for the pre-trained model (only car model provided for now).
$ python3 test.py -n pre_trained_car -b 1
results will be dumped into predictions/data
. Set the --vis
flag to True if dumping visualizations and they will be saved into predictions/vis
.
- run the following command to measure quantitative performances of predictions:
./kitti_eval/evaluate_object_3d_offline [DATA_DIR]/validation/label_2 ./predictions
The current implementation and training scheme are able to produce results in the tables below.
Car | Easy | Moderate | Hard |
---|---|---|---|
Reported | 89.60 | 84.81 | 78.57 |
Reproduced | 85.41 | 83.16 | 77.10 |
Car | Easy | Moderate | Hard |
---|---|---|---|
Reported | 81.97 | 65.46 | 62.85 |
Reproduced | 53.43 | 48.78 | 48.06 |
- improve the performances
- reproduce results for
Pedestrian
andCyclist
- fix the deadlock problem in multi-thread processing in training
- fix the infinite loop problem in
test.py
- replace averaged calibration matrices with correct ones