PointPillars: Fast Encoders for Object Detection from Point Clouds

A Simple PointPillars PyTorch Implenmentation for 3D Lidar(KITTI) Detection. [Zhihu]

It can be run without installing Spconv, mmdet or mmdet3d.
Only one detection network (PointPillars) was implemented in this repo, so the code may be more easy to read.
Sincere thanks for the great open-souce architectures mmcv, mmdet and mmdet3d, which helps me to learn 3D detetion and implement this repo.

mAP on KITTI validation set (Easy, Moderate, Hard)

Repo	Metric	Overall	Pedestrian	Cyclist	Car
this repo	3D-BBox	73.3259 62.7834 59.6278	51.4642 47.9446 43.8040	81.8677 63.6617 60.9126	86.6456 76.7439 74.1668
mmdet3d v0.18.1	3D-BBox	72.0537, 60.1114, 55.8320	52.0263, 46.4037, 42.4841	78.7231, 59.9526, 57.2489	85.4118, 73.9780, 67.7630
this repo	BEV	77.8540 69.8003 66.6699	59.1687 54.3456 50.5023	84.4268 67.1409 63.7409	89.9664 87.9145 85.7664
mmdet3d v0.18.1	BEV	76.6485, 67.7609, 64.5605	59.0778, 53.3638, 48.4230	80.9328, 63.3447, 60.0618	89.9348, 86.5743, 85.1967
this repo	2D-BBox	80.5097 74.6120 71.4758	64.6249 61.4201 57.5965	86.2569 73.0828 70.1726	90.6471 89.3330 86.6583
mmdet3d v0.18.1	2D-BBox	78.4938, 73.4781, 70.3613	62.2413, 58.9157, 55.3660	82.6460, 72.3547, 68.4669	90.5939, 89.1638, 87.2511
this repo	AOS	74.9647 68.1712 65.2817	49.3777 46.7284 43.8352	85.0412 69.1024 66.2801	90.4752 88.6828 85.7298
mmdet3d v0.18.1	AOS	72.41, 66.23, 63.55	46.00, 43.22, 40.94	80.85, 67.20, 63.63	90.37, 88.27, 86.07

Note: Here, we report mmdet3d v0.18.1 (2022/02/09-2022/03/01) performance based on the officially provided checkpoint. Much improvements were made in the mmdet3d v1.0.0rc1.

Detection Visualization

[Datasets]

Download

Download point cloud(29GB), images(12 GB), calibration files(16 MB)和labels(5 MB)。Format the datasets as follows:

kitti
    |- training
        |- calib (#7481 .txt)
        |- image_2 (#7481 .png)
        |- label_2 (#7481 .txt)
        |- velodyne (#7481 .bin)
    |- testing
        |- calib (#7518 .txt)
        |- image_2 (#7518 .png)
        |- velodyne (#7418 .bin)

Pre-process KITTI datasets First

cd PointPillars/
python pre_process_kitti.py --data_root your_path_to_kitti

Now, we have datasets as follows:

kitti
    |- training
        |- calib (#7481 .txt)
        |- image_2 (#7481 .png)
        |- label_2 (#7481 .txt)
        |- velodyne (#7481 .bin)
        |- velodyne_reduced (#7481 .bin)
    |- testing
        |- calib (#7518 .txt)
        |- image_2 (#7518 .png)
        |- velodyne (#7518 .bin)
        |- velodyne_reduced (#7518 .bin)
    |- kitti_gt_database (# 19700 .bin)
    |- kitti_infos_train.pkl
    |- kitti_infos_val.pkl
    |- kitti_infos_trainval.pkl
    |- kitti_infos_test.pkl
    |- kitti_dbinfos_train.pkl

[Compile]

cd ops
python setup.py develop

[Training]

cd PointPillars/
python train.py --data_root your_path_to_kitti

[Evaluation]

cd PointPillars/
python evaluate.py --ckpt pretrained/epoch_160.pth --data_root your_path_to_kitti

[Test]

cd PointPillars/

# 1. infer and visualize point cloud detection
python test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path 

# 2. infer and visualize point cloud detection and gound truth.
python test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path --calib_path your_calib_path

# 3. infer and visualize point cloud & image detection
python test.py --ckpt pretrained/epoch_160.pth --pc_path your_pc_path --calib_path your_calib_path --img_path your_img_path


e.g. [infer on val set 000134]

python test.py --ckpt pretrained/epoch_160.pth --pc_path /home/lifa/data/KITTI/training/velodyne_reduced/000134.bin

or

python test.py --ckpt pretrained/epoch_160.pth --pc_path /home/lifa/data/KITTI/training/velodyne_reduced/000134.bin --calib_path /home/lifa/data/KITTI/training/calib/000134.txt --img_path /home/lifa/data/KITTI/training/image_2/000134.png --gt_path /home/lifa/data/KITTI/training/label_2/000134.txt

Acknowledements

Thanks for the open souce code mmcv, mmdet and mmdet3d.

iamwilliamli/PointPillars