Skip to content
(CVPR 2019) Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
Branch: master
Clone or download
Latest commit 5905da0 May 18, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs Update index.html Jan 16, 2019
figures release code Apr 26, 2019
preprocessing release code Apr 26, 2019
psmnet release code Apr 26, 2019
.gitignore Initial commit Jan 4, 2019
.gitmodules add avod and frustum-pointnet Mar 28, 2019
LICENSE Initial commit Jan 4, 2019
PATENTS.txt Create PATENTS.txt May 18, 2019
README.md release code Apr 26, 2019

README.md

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

This paper has been accpeted by Conference on Computer Vision and Pattern Recognition (CVPR) 2019.

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

by Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell and Kilian Q. Weinberger

Figure

Citation

@inproceedings{wang2019pseudo,
  title={Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving},
  author={Wang, Yan and Chao, Wei-Lun and Garg, Divyansh and Hariharan, Bharath and Campbell, Mark and Weinberger, Kilian},
  booktitle={CVPR},
  year={2019}
}

Contents

Introduction

3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies --- a gap that is commonly attributed to poor image-based depth estimation. However, in this paper we argue that data representation (rather than its quality) accounts for the majority of the difference. Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations --- essentially mimicking LiDAR signal. With this representation we can apply different existing LiDAR-based detection algorithms. On the popular KITTI benchmark, our approach achieves impressive improvements over the existing state-of-the-art in image-based performance --- raising the detection accuracy of objects within 30m range from the previous state-of-the-art of 22% to an unprecedented 74%. At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo image based approaches.

Usage

1. Overview

We provide the guidance and codes to train stereo depth estimator and 3D object detector using the KITTI object detection benchmark. We also provide our pre-trained models.

2. Stereo depth estimation models

We provide our pretrained PSMNet model using the Scene Flow dataset and the 3,712 training images of the KITTI detection benchmark.

We also directly provide the pseudo-LiDAR point clouds and the ground planes of training and testing images estimated by this pre-trained model.

We also provide codes to train your own stereo depth estimator and prepare the point clouds and gound planes. If you want to use our pseudo-LiDAR data for 3D object detection, you may skip the following contents and directly move on to object detection models.

2.1 Dependencies

  • Python 3.5+
  • numpy, scikit-learn, scipy
  • KITTI 3D object detection dataset

2.2 Download the dataset

You need to download the KITTI dataset from here, including left and right color images, Velodyne point clouds, camera calibration matrices, and training labels. You also need to download the image set files from here. Then you need to organize the data in the following way.

KITTI/object/
    
    train.txt
    val.txt
    test.txt 
    
    training/
        calib/
        image_2/ #left image
        image_3/ #right image
        label_2/
        velodyne/ 

    testing/
        calib/
        image_2/
        image_3/
        velodyne/

The Velodyne point clouds (by LiDAR) are used ONLY as the ground truths to train a stereo depth estimator (e.g., PSMNet).

2.3 Generate ground-truth image disparities

Use the script./preprocessing/generate_disp.py to process all velodyne files appeared in train.txt. This is our training ground truth. Or you can directly download them from disparity. Name this folder as disparity and put it inside the training folder.

python generate_disp.py --data_path ./KITTI/object/training/ --split_file ./KITTI/object/train.txt 

2.4. Train the stereo model

You can train any stereo disparity model as you want. Here we give an example to train the PSMNet. The modified code is saved in the subfolder psmnet. Make sure you follow the README inside this folder to install the correct python and library. I strongly suggest using conda env to organize the python environments since we will use Python with different versions. Download the psmnet model pretrained on Sceneflow dataset from here.

# train psmnet with 4 TITAN X GPUs.
python ./psmnet/finetune_3d.py --maxdisp 192 \
     --model stackhourglass \
     --datapath ./KITTI/object/training/ \
     --split_file ./KITTI/object/train.txt \
     --epochs 300 \
     --lr_scale 50 \
     --loadmodel ./pretrained_sceneflow.tar \
     --savemodel ./psmnet/kitti_3d/  --btrain 12

2.5 Predict the point clouds

Predict the disparities.
# training
python ./psmnet/submission.py \
    --loadmodel ./psmnet/kitti_3d/finetune_300.tar \
    --datapath ./KITTI/object/training/ \
    --save_path ./KITTI/object/training/predict_disparity
# testing
python ./psmnet/submission.py \
    --loadmodel ./psmnet/kitti_3d/finetune_300.tar \
    --datapath ./KITTI/object/testing/ \
    --save_path ./KITTI/object/testing/predict_disparity
Convert the disparities to point clouds.
# training
python ./preprocessing/generate_lidar.py  \
    --calib_path ./KITTI/object/training/calib/ \
    --save_path ./KITTI/object/training/pseudo-lidar_velodyne/ \
    --disp_path ./KITTI/object/training/predict_disparity \
    --max_high 1
# testing
python ./preprocessing/generate_lidar.py  \
    --calib_path ./KITTI/object/testing/calib/ \
    --save_path ./KITTI/object/testing/pseudo-lidar_velodyne/ \
    --disp_path ./KITTI/object/testing/predict_disparity \
    --max_high 1

2.6 Generate ground plane

If you want to train an AVOD model for 3D object detection, you need to generate ground planes from pseudo-lidar point clouds.

#training
python ./preprocessing/kitti_process_RANSAC.py \
    --calib ./KITTI/object/training/calib/ \
    --lidar_dir  ./KITTI/object/training/pseudo-lidar_velodyne/ \
    --planes_dir /KITTI/object/training/pseudo-lidar_planes/
#testing
python ./preprocessing/kitti_process_RANSAC.py \
    --calib ./KITTI/object/testing/calib/ \
    --lidar_dir  ./KITTI/object/testing/pseudo-lidar_velodyne/ \
    --planes_dir /KITTI/object/testing/pseudo-lidar_planes/

3. Object Detection models

AVOD model

Download the code from https://github.com/kujason/avod and install the Python dependencies.

Follow their README to prepare the data and then replace (1) files in velodyne with those in pseudo-lidar_velodyne and (2) files in planes with those in pseudo-lidar_planes. Note that you should still keep the folder names as velodyne and planes.

Follow their README to train the pyramid_cars_with_aug_example model. You can also download our pretrained model and directly evaluate on it. But if you want to submit your result to the leaderboard, you need to train it on trainval.txt.

Frustum-PointNets model

Download the code from https://github.com/charlesq34/frustum-pointnets and install the Python dependencies.

Follow their README to prepare the data and then replace files in velodyne with those in pseudo-lidar_velodyne. Note that you should still keep the folder name as velodyne.

Follow their README to train the v1 model. You can also download our pretrained model and directly evaluate on it.

Results

The main results on the validation dataset of our pseudo-LiDAR method. Figure

Contact

If you have any question, please feel free to email us.

Yan Wang (yw763@cornell.edu), Harry Chao(weilunchao760414@gmail.com), Div Garg(dg595@cornell.edu)

You can’t perform that action at this time.