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Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection (NeurIPS '21, Official Repo)

John Lambert, James Hays

This repository contains the source code for training and evaluating models described in the NeurIPS '21 paper Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection. [arXiv] [Project Site & Videos]

The Trust but Verify (TbV) dataset is publicly available for download, as part of the Argoverse 2.0 family of datasets. Download instructions can be found here. You can find a short invited talk at the CVPR 2021 VOCVALC workshop summarizing our work here on Youtube.

Table of Contents

Dataset Overview

The Trust but Verify (TbV) dataset is the first public dataset for the task of high-definition (HD) map change detection, i.e. determining when sensor data and map data are no longer in agreement with one another due to real-world changes. We collected TbV by mining thousands of hours of data from over 9 months of autonomous vehicle fleet operations.

An example from Pittsburgh:

Examples from Palo Alto and Miami:

Examples from Miami:

The dataset, consisting of maps and logs collected in six North American cities, is one of the largest AV datasets to date with more than 7.9 million images and will be made available to the public, along with code and models under the the CC BY-NC-SA 4.0 license. Above, we show before-and-after images that showcase a few examples of map changes featured in TbV logs.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Installation

First, clone the repo:

git clone https://github.com/johnwlambert/tbv.git

Next, install Miniconda or Anaconda, and create the conda environment:

conda env create -f environment_linux.yml
conda env create -f environment_mac.yml

Note: rendering data is only supported on Linux with a CUDA-supported GPU.

cd tbv
pip install -e .

When you clone the repo, the structure should be as follows:

- tbv/
 |--- setup.py
 |--- tbv-raytracing/
   |---setup.py
   |---pybind11/

Next, install mseg-api anywhere on your machine using:

git clone https://github.com/mseg-dataset/mseg-api.git
cd mseg-api
pip install -e .
cd ..

Next, install mseg-semantic anywhere on your machine using:

git clone https://github.com/mseg-dataset/mseg-semantic.git
pip install -e .

Next, install av2 (av2-api is the official repo for Argoverse 2.0):

pip install av2==0.1.0

Next, install Eigen. On Linux, sudo apt install libeigen3-dev. Next, cd tbv-raytracing and download pybind11 via git clone https://github.com/pybind/pybind11.git, where it should be downloaded into the second-level tbv-raytracing dir.

Ensure your nvcc compiler is at least version 11.3, V11.3.109, and then compile the GPU library using setup.py as follows:

python setup.py bdist_wheel
pip install dist/tbv_raytracing-0.0.1-cp{PY_VERSION}-cp{PY_VERSION}-linux_x86_64.whl

e.g. this file could be named one of the following:

pip install dist/tbv_raytracing-0.0.1-cp38-cp38-linux_x86_64.whl
pip install dist/tbv_raytracing-0.0.1-cp39-cp39-linux_x86_64.whl 
pip install dist/tbv_raytracing-0.0.1-cp310-cp310-linux_x86_64.whl

Downloading the dataset

Download the dataset per the instructions found here.

Create a folder, and then logs/

Rendering Training/Val/Test Data

To render data in a bird's eye view, run

python scripts/run_dataset_rendering_job.py --config_name bev_config.yaml

To render data in the ego-view, run

python scripts/run_dataset_rendering_job.py --config_name egoview_config.yaml

For training data w/ augmentations, ensure the following fields are set to render_test_set_only: False and jitter_vector_map: True.

Program output will be saved in a logging_output directory.

We use the following abbreviations for city names featured in TbV:

City Name Abbreviation
Washington, DC WDC
Miami, FL MIA
Pittsburgh, PA PIT
Palo Alto, CA PAO
Austin, TX ATX
Detroit, MI DTW

Training Models

After rendering a dataset, you're ready to train models. Start training by running:

python scripts/train.py \
    --training_config_name {CONFIG_UUID}.yaml \
    --rendering_config_name {CONFIG_UUID}.yaml

Evaluating Models

Pretrained Models are available here. Each model has an associated rendering config, training config, and model checkpoint file, all with the same uuid (e.g. 0589cca2-72aa-4626-9a05-af60eeea5fb6).

To run model inference with a model trained to operate on the ego-view:

python scripts/test.py \
    --rendering_config_name 6d3bfc13-1da4-49f0-bf6d-f6b1fc9647d8.yaml \
    --training_config_name 6d3bfc13-1da4-49f0-bf6d-f6b1fc9647d8.yaml \
    --gpu_ids 0 \
    --save_inference_viz False \
    --split val \
    --ckpt_fpath ~/Downloads/6d3bfc13-1da4-49f0-bf6d-f6b1fc9647d8.pth \
    --filter_eval_by_visibility True

To run model inference with a model trained to operate on the BEV:

python scripts/test.py \
    --rendering_config_name 0589cca2-72aa-4626-9a05-af60eeea5fb6.yaml \
    --training_config_name 0589cca2-72aa-4626-9a05-af60eeea5fb6.yaml \
    --gpu_ids 0 \
    --save_inference_viz False \
    --split val \
    --ckpt_fpath ~/Downloads/0589cca2-72aa-4626-9a05-af60eeea5fb6.pth \
    --filter_eval_by_visibility False \

Pre-trained Model Accuracies

Below, we provide the accuracies of the released pre-trained models on the val and test sets. Mean accuracies below are over the visible region (see asterisk *). All use early-fusion, except otherwise specified.

Model UUID Model Description Sensor Input Map Input Semantic Label Map Input (Real) Val mAcc* (Real) Test mAcc*
6d3bfc13-1da4-49f0-bf6d-f6b1fc9647d8 egoview, dropout either map or semantics, 100% prob ✅ ✅ ✅ 0.7031 0.7013
9dcfaa3f-a6af-4295-93ca-24a84d6b9c2d egoview, 224x224, larger batch size ✅ ✅ ✅ 0.6916 0.6843
98e50a71-c7b2-411d-99f4-781826488a26 egoview, blurred input, independent map dropout_prob: 0.5, independent semantics dropout prob: 0.0 ✅ ✅ ✅ 0.7138 0.6826
2a3550a4-7b3d-4ab1-8165-e20d7cb069c9 egoview, early fusion, but dropout either semantics or sensor, 0% prob ✅ ✅ ✅ 0.6923 0.6747
0061c32d-da98-4583-a311-8f2fc37b6655 egoview, independent map dropout_prob: 0.0, independent semantics dropout prob: 0.5 ✅ ✅ ✅ 0.6850 0.6697
44e55ee6-76da-4995-8fff-f4a2a4c3a8af egoview, independent semantics dropout prob: 0.75, independent map dropout prob: 0.75 ✅ ✅ ✅ 0.6735 0.6766
4d4f41a2-4bfe-42f2-88d0-1db253eeb9be egoview, high res, all 3 modalities w/ dropout, 448x448 ✅ ✅ ✅ 0.6732 0.6589
17fd2c0a-fee5-47c5-92cc-8b37f4479a8b egoview, independent semantics dropout prob: 0.75, independent map dropout prob: 0.50 ✅ ✅ ✅ 0.6683 0.6606
b3ef41e8-db72-4e12-808d-353e4cd54280 egoview 224x224 ✅ ✅ 0.6677 0.6183
88f42746-3374-40f7-a015-625652ca62c8 egoview, blurred, independent semantics dropout prob: 0.25, independent map dropout prob: 0.25 ✅ ✅ ✅ 0.6762 0.6604
fe460247-d73f-4519-8643-ff38f95fb3b7 egoview, 0% dropout, and with blur ✅ ✅ ✅ 0.6781 0.6373
e3411e4e-87a6-4539-9ebb-1fcd6f99e601 egoview, Resnet-50 ✅ ✅ 0.6505 0.6442
36b24988-5c54-46b4-9c22-cd48f70ae9f6 egoview, 120 epochs ✅ ✅ 0.6533 0.6169
9b170dcf-6ba8-41d0-9ff2-fc3faf92e514 egoview, 60 epochs, no multiple negatives ✅ ✅ 0.6085 0.6362
b5a2198c-5368-4feb-85c5-843a5646ecfa egoview, map-only ✅ 0.5512 0.5364
615683e4-8431-4b31-ba1d-3523e6165fa9 egoview, late fusion ✅ ✅ 0.5453 0.4963
0589cca2-72aa-4626-9a05-af60eeea5fb6 BEV, Resnet-50, 30 epochs ✅ ✅ 0.6588 0.6448
e0ac04a5-c883-4497-81ef-e89ef3d23fdb BEV, late fusion ✅ ✅ 0.6207 0.5450

Generating seamseg semantic segmentation label maps

Some models use seamseg label maps. To generate them, follow the steps below:

Clone the following fork of seamseg: https://github.com/johnwlambert/seamseg

Download the seamseg seamseg_r50_vistas.zip model here, or using the following bash commands:

export GDRIVE_FILEID='1ULhd_CZ24L8FnI9lZ2H6Xuf03n6NA_-Y'
export GDRIVE_URL='https://docs.google.com/uc?export=download&id='$GDRIVE_FILEID
wget --save-cookies cookies.txt $GDRIVE_URL -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1/p' > confirm.txt
wget --load-cookies cookies.txt -O seamseg_r50_vistas.zip $GDRIVE_URL'&confirm='$(<confirm.txt)

Install inplace-abn: pip install git+https://github.com/mapillary/inplace_abn.git, and unzip the downloaded .zip file:

mkdir seamseg_pretrained_models
unzip seamseg_r50_vistas.zip -d seamseg_pretrained_models

You should see

ls -l seamseg_pretrained_models 
config.ini
metadata.bin
seamseg_r50_vistas.tar

Pass /path/to/seamseg_pretrained_models as seamseg_model_dirpath to python run_seamseg_over_logs.py as follows:

cd seamseg/scripts
mkdir ../logs
python ../../tbv-staging/scripts/run_seamseg_over_logs.py --tbv-dataroot /tbv_dataset/logs_raw --seamseg_output_dataroot /tbv_dataset/seamseg_output --num-processes 1 --split test --seamseg_model_dirpath /path/to/seamseg_pretrained_models

Citing this work

@inproceedings{Lambert21neurips_TrustButVerifyHDMapChangeDetection,
 author = {Lambert, John W. and Hays, James},
 booktitle = {Advances in Neural Information Processing Systems Track on Datasets and Benchmarks},
 title = {{Trust, but Verify}: Cross-Modality Fusion for HD Map Change Detection},
 url = {https://openreview.net/pdf?id=cXCZnLjDm4s},
 year = {2021}
}

FAQ:

Q: Is there a file that indicates different log pairs and what has changed?

A: We provide a clustering of logs by spatial location here. A few things to note:

  • Each log within a cluster shares some significant visual overlap with other logs within its cluster.
  • These are not necessarily before/after pairs. In some cases, all logs in a cluster may be "after" a change.
  • Each cluster has at least one log in the val or test set.
  • Logs of each cluster are provided in chronological order.

Q: Where can I find the data splits?

A: Official train, val, test data splits are available here. There are 799 train logs, 111 val logs, and 133 test logs.

Q: Will labels be released for which logs include change/no change? Wanted to verify that the training sets have no changes. Therefore, all we need to know is if a log is in the training set to know the label?

A: Yes. Val split labels can be found here. All train logs are positive logs that contain no changes. Most of the val and test logs contain at least some change (negatives), although some are positive "before" logs.

Q: TbV doesn't necessarily have before and after sensor data? So it seems we're just checking if the corresponding vector map is up-to-date or not?

A: Correct. We certainly do have many pairs of sensor data before/after in the dataset, but our goal was to be able to not have to store all past sensor data when we want to make an online map change prediction (for the TbV paper's experiments, we assume online online sensor data, and an onboard map).

Q: How you make your decision on change: In your paper, you mention that each change task is given a buffer of sensor data from time 0 to t, but in your model architectures in figure 3, I can't figure out how you incorporate the buffer. Is it at each time stamp, a change decision is made, and then you average the decision from all of the time stamps? A: Using the buffer is not strictly necessary, but in some cases, it can be useful to have. Having a buffer of past info is also fairly realistic w.r.t. onboard settings. For the bird's eye view models we trained, we used a ring buffer to keep around the past 3d points w/ their RGB values, to make a richer input texture map (see code here). For the ego-view models, we didn't use a buffer of sensor data, but there would be ways to feed into a buffer of data as input. We discuss this a bit Appendix F, page 5 of the supplement.

Q: I can't compile the tbv_raytracing package? A: Check that the version of your driver (cat /proc/driver/nvidia/version) is compatible with your cuda-toolkit version (torch.version.cuda), according to the NVIDIA compatibility CUDA/driver docs.

License

All code within this repository and all data included in the TbV Dataset are governed by the CC BY-NC-SA 4.0 license. By downloading the software, you are agreeing to the terms of this license agreement. If you do not agree with these terms, you may not use or download this software. It may not be used for any commercial purpose. See LICENSE for more details.

This code and dataset are owned by Argo AI, LLC (Licensor), but are distributed by John Lambert with Argo's permission under CC BY-NC-SA 4.0.

Exclusive Remedy and Limitation of Liability: To the maximum extent permitted under applicable law, Licensor shall not be liable for direct, indirect, special, incidental, or consequential damages or lost profits related to Licensee's (you or your organization) use of and/or inability to use the Software, even if Licensor is advised of the possibility of such damage.

Disclaimer of warranties: The software is provided "as-is" without warranty of any kind including any warranties of performance or merchantability or fitness for a particular use or purpose or of non-infringement. Licensee bears all risk relating to quality and performance of the software and related materials.

Copyright: The Software is owned by Licensor and is protected by United States copyright laws and applicable international treaties and/or conventions.