A pytorch version of frustum-pointnets (Not support Pointnet++ yet)
main function of f-pointnets now: train/train_fpointnets.py, train/test_fpointnets.py, train/provider_fpointnet.py, models/frustum_pointnets_v1_old.py model_util_old.py kitti/*
Test on
- Ubuntu-18.04
- CUDA-10.0
- Pytorch 1.3
- python 3.7
Some visulization demos need mayavi, it would be a little bit difficult to install it. I install mayavi(python3) by: (ref:http://docs.enthought.com/mayavi/mayavi/installation.html#installing-with-pip)
1. download vtk(https://vtk.org/download/) and compile:
unzip VTK
cd VTK
mkdir build
cd build
cmake ..
make
sudo make install
2. install mayavi and PyQt5
pip install mayavi
pip install PyQt5
frustum_pointnets_pytorch
├── dataset
│ ├── KITTI
│ │ ├── ImageSets
│ │ ├── object
│ │ │ ├──training
│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│ │ │ ├──testing
│ │ │ ├──calib & velodyne & image_2
│ ├── nuScenes
│ │ ├── v1.0-mini
│ │ │ ├──maps & samples & sweeps & v1.0-mini
│ │ ├── v1.0-trainval
│ │ ├── v1.0-test
│ ├── nuScenes2kitti
│ │ ├── v1.0-mini
│ │ ├── training
│ │ ├── testing
├── kitti
│ │ ├── image_sets
│ │ ├── rgb_detections
├── nuscenes2kitti
│ │ ├── image_sets
│ │ ├── rgb_detections(TODO)
├── train
To visulize single sample in Kitti
python kitti/prepare_data.py --demo
To prepare training data
python kitti/prepare_data.py --gen_train --gen_val --gen_val_rgb_detection --car_only
To visulize all gt boxes and prediction boxes:
python kitti/kitti_object.py
TODO
First, convert nuScenes to kitti(only consider CAM_FRONT)
python nuscenes2kitti/nuscenes_convert_full_samples.py --version v1.0-mini --CAM_FRONT_only
python nuscenes2kitti/nuscenes_convert_full_samples.py --version v1.0-trainval --CAM_FRONT_only --number 7481
After conversion, you can visulize them by
python nuscenes2kitti/prepare_data.py --demo
Then, generate *.pickle training data and write to nuscenes2kitti by
python nuscenes2kitti/prepare_data.py --gen_train --gen_val --gen_mini --car_only --CAM_FRONT_only
CUDA_VISIBLE_DEVICES=0 python train/train_fpointnets.py --log_dir log
CUDA_VISIBLE_DEVICES=0 python train/train_fpointnets.py --dataset nuscenes2kitti
CUDA_VISIBLE_DEVICES=0 python train/test_fpointnets.py --model_path <log/.../xx.pth> --output test_results
train/kitti_eval/evaluate_object_3d_offline dataset/KITTI/object/training/label_2/ test_results
python kitti/kitti_object.py
(score_list.append(batch_rgb_prob[j])))
car AP @0.70, 0.70, 0.70:
bbox AP:96.48, 90.31, 87.63
bev AP:88.57, 84.78, 76.79
3d AP:85.09, 72.11, 64.25
(using f-pointnets scores(max positive score of segmentation mask here, check test.py))
test segmentation accuracy: 0.901991
test box IoU(ground/3D): 0.796272/0.744235
test box estimation accuracy (IoU=0.7): 0.766470
(batch_scores = mask_max_prob)
car AP @0.70, 0.70, 0.70:
bbox AP:100.00,100.00,100.00
bev AP:87.66, 85.59, 77.76
3d AP:84.79, 74.12, 66.64
(test from gt leads to 2~3 AP decrese, because f-pointnets cannot output a good "score")
train per batch: 6.00ms/32 objects
test per batch: 15.21ms/32 objects
test per object: 135.83ms/object
python train/train_fpointnets.py --name three
**** EPOCH 150 ****
Epoch 150/150:
100%|███████████████████████████████████████████████████| 1851/1851 [05:08<00:00, 6.00it/s]
[150: 1850/1851] train loss: 0.051460
segmentation accuracy: 0.960588
box IoU(ground/3D): 0.845676/0.790897
box estimation accuracy (IoU=0.7): 0.880115
100%|█████████████████████████████████████████████████████| 392/392 [00:25<00:00, 15.21it/s]
[150] test loss: 0.106460
test segmentation accuracy: 0.901991
test box IoU(ground/3D): 0.796272/0.744235
test box estimation accuracy (IoU=0.7): 0.766470
learning rate: 0.000082
Best Test acc: 0.769261(Epoch 130)
Time 14.12774204615861 hours
model saved to log/three_caronly_kitti_2020-03-05-16/three_caronly_kitti_2020-03-05-16/acc0.769261-epoch129.pth
python train/test_fpointnets.py --output output/default --model_path log/three_caronly_kitti_2020-03-05-16/three_caronly_kitti_2020-03-05-16/acc0.769261-epoch129.pth
train/kitti_eval/evaluate_object_3d_offline dataset/KITTI/object/training/label_2/ output/default/
car_detection AP: 100.000000 100.000000 100.000000
car_detection_ground AP: 87.663536 85.591621 77.763123
car_detection_3d AP: 84.790321 74.122635 66.642036
python train/test_fpointnets.py --output output/gt2rgb --model_path log/three_caronly_kitti_2020-03-05-16/three_caronly_kitti_2020-03-05-16/acc0.769261-epoch129.pth --data_path kitti/frustum_caronly_val_rgb_detection.pickle --from_rgb_det
train/kitti_eval/evaluate_object_3d_offline dataset/KITTI/object/training/label_2/ output/gt2rgb/
using f-pointnets scores(use max positive score of segmentation mask here, check test.py)
car_detection AP: 94.753860 86.358948 84.424759
car_detection_ground AP: 85.699196 80.538277 73.625732
car_detection_3d AP: 81.882027 70.021523 63.075848
using rgb detection scores
car_detection AP: 96.482544 90.305161 87.626389
car_detection_ground AP: 87.814728 82.850800 75.781403
car_detection_3d AP: 84.952042 72.118301 64.253830
- features
- pointnet++
- models
- frustum-convnet
- extend_rgb,globalfusion,densefusion,pointpaint,...
- datasets
- nuScenes2kitti
- SUN-RGBD
many codes are from: