• Stars
    star
    291
  • Rank 139,104 (Top 3 %)
  • Language
    Python
  • License
    MIT License
  • Created almost 3 years ago
  • Updated over 1 year ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

[ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving

NEAT: Neural Attention Fields for End-to-End Autonomous Driving

Paper | Supplementary | Video | Talk | Poster | Slides

This repository is for the ICCV 2021 paper NEAT: Neural Attention Fields for End-to-End Autonomous Driving.

@inproceedings{Chitta2021ICCV,
  author = {Chitta, Kashyap and Prakash, Aditya and Geiger, Andreas},
  title = {NEAT: Neural Attention Fields for End-to-End Autonomous Driving},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year = {2021}
}

Setup

Please follow the installation instructions from our TransFuser repository to set up the CARLA simulator. The conda environment required for NEAT can be installed via:

conda env create -f environment.yml
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia

For running the AIM-VA baseline, you will additionally need to install MMCV and MMSegmentation.

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
pip install mmsegmentation

Data Generation

The training data is generated using leaderboard/team_code/auto_pilot.py. Data generation requires routes and scenarios. Each route is defined by a sequence of waypoints (and optionally a weather condition) that the agent needs to follow. Each scenario is defined by a trigger transform (location and orientation) and other actors present in that scenario (optional). We provide several routes and scenarios under leaderboard/data/. The TransFuser repository and leaderboard repository provide additional routes and scenario files.

Running a CARLA Server

With Display

./CarlaUE4.sh --world-port=2000 -opengl

Without Display

Without Docker:

SDL_VIDEODRIVER=offscreen SDL_HINT_CUDA_DEVICE=0 ./CarlaUE4.sh --world-port=2000 -opengl

With Docker:

Instructions for setting up docker are available here. Pull the docker image of CARLA 0.9.10.1 docker pull carlasim/carla:0.9.10.1.

Docker 18:

docker run -it --rm -p 2000-2002:2000-2002 --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 carlasim/carla:0.9.10.1 ./CarlaUE4.sh --world-port=2000 -opengl

Docker 19:

docker run -it --rm --net=host --gpus '"device=0"' carlasim/carla:0.9.10.1 ./CarlaUE4.sh --world-port=2000 -opengl

If the docker container doesn't start properly then add another environment variable -e SDL_AUDIODRIVER=dsp.

Running the Autopilot

Once the CARLA server is running, rollout the autopilot to start data generation.

./leaderboard/scripts/run_evaluation.sh

The expert agent used for data generation is defined in leaderboard/team_code/auto_pilot.py. Different variables which need to be set are specified in leaderboard/scripts/run_evaluation.sh. The expert agent is originally based on the autopilot from this codebase.

Training

The training code and pretrained models are provided below.

mkdir model_ckpt
wget https://s3.eu-central-1.amazonaws.com/avg-projects/neat/models.zip -P model_ckpt
unzip model_ckpt/models.zip -d model_ckpt/
rm model_ckpt/models.zip

There are 5 pretrained models provided in model_ckpt/:

Additional baselines are available in the TransFuser repository.

Evaluation

Spin up a CARLA server (described above) and run the required agent. The required variables need to be set in leaderboard/scripts/run_evaluation.sh.

CUDA_VISIBLE_DEVICES=0 ./leaderboard/scripts/run_evaluation.sh

Acknowledgements

This implementation primarily extends the cvpr2021 branch of the existing TransFuser repository.

If you found our work interesting, check out the code for some more recent work on CARLA from our group:

More Repositories

1

sdfstudio

A Unified Framework for Surface Reconstruction
Python
1,861
star
2

occupancy_networks

This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"
Python
1,454
star
3

giraffe

This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"
Python
1,227
star
4

stylegan-t

[ICML'23] StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
Python
1,122
star
5

transfuser

[PAMI'23] TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving; [CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
Python
957
star
6

stylegan-xl

[SIGGRAPH'22] StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
Python
939
star
7

projected-gan

[NeurIPS'21] Projected GANs Converge Faster
Python
876
star
8

unimatch

[TPAMI'23] Unifying Flow, Stereo and Depth Estimation
Python
855
star
9

convolutional_occupancy_networks

[ECCV'20] Convolutional Occupancy Networks
Python
792
star
10

differentiable_volumetric_rendering

This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"
Python
782
star
11

mip-splatting

[CVPR'24 Oral] Mip-Splatting: Alias-free 3D Gaussian Splatting
Python
700
star
12

monosdf

[NeurIPS'22] MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
Python
535
star
13

shape_as_points

[NeurIPS'21] Shape As Points: A Differentiable Poisson Solver
Python
518
star
14

unisurf

[ICCV'21] UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
Python
410
star
15

graf

Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"
Jupyter Notebook
393
star
16

tuplan_garage

[CoRL'23] Parting with Misconceptions about Learning-based Vehicle Motion Planning
Python
370
star
17

kitti360Scripts

This repository contains utility scripts for the KITTI-360 dataset.
Python
353
star
18

gaussian-opacity-fields

Gaussian Opacity Fields for Efficient and Compact Surface Reconstruction in Unbounded Scenes
Python
285
star
19

occupancy_flow

This repository contains the code for the ICCV 2019 paper "Occupancy Flow - 4D Reconstruction by Learning Particle Dynamics"
Python
207
star
20

plant

[CoRL'22] PlanT: Explainable Planning Transformers via Object-Level Representations
Python
192
star
21

factor-fields

[SIGGRAPH 2023] We provide a unified formula for neural fields (Factor Fields) and a novel dictionary factorization (Dictionary Fields)
Jupyter Notebook
183
star
22

voxgraf

Official code release for VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
Python
123
star
23

carla_garage

[ICCV'23] Hidden Biases of End-to-End Driving Models
Python
121
star
24

texture_fields

This repository contains code for the paper 'Texture Fields: Learning Texture Representations in Function Space'.
Python
113
star
25

sledge

SLEDGE: Synthesizing Simulation Environments for Driving Agents with Generative Models
105
star
26

kitti360LabelTool

JavaScript
103
star
27

counterfactual_generative_networks

[ICLR'21] Counterfactual Generative Networks
Python
102
star
28

gta

[ICLR'24] GTA: A Geometry-Aware Attention Mechanism for Multi-view Transformers
Python
95
star
29

murf

[CVPR'24] MuRF: Multi-Baseline Radiance Fields
Python
84
star
30

controllable_image_synthesis

Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis, CVPR 2020
Python
69
star
31

king

[ECCV'22] KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
Python
61
star
32

handheld_svbrdf_geometry

On Joint Estimation of Pose, Geometry and svBRDF from a Handheld Scanner, CVPR2020
Python
57
star
33

navsim

NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation
Python
52
star
34

connecting_the_dots

This repository contains the code for the paper "Connecting the Dots: Learning Representations for Active Monocular Depth Estimation" https://avg.is.tuebingen.mpg.de/publications/riegler2019cvpr
Python
51
star
35

frequency_bias

Official code for "On the Frequency Bias of Generative Models", NeurIPS 2021
Python
39
star
36

data_aggregation

This repository contains the code for the CVPR 2020 paper "Exploring Data Aggregation in Policy Learning for Vision-based Urban Autonomous Driving"
Python
38
star
37

good

[ICLR'23] GOOD: Exploring Geometric Cues for Detecting Objects in an Open World
Python
36
star
38

campari

[3DV'21] CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields
Python
29
star
39

autonomousvision.github.io

Blog of the Autonomous Vision Group at MPI-IS Tübingen and University of Tübingen.
HTML
19
star
40

visual_abstractions

6
star
41

slides

Slide repository of the Autonomous Vision Group at MPI-IS Tübingen and University of Tübingen.
CSS
2
star
42

similarity_reconstruction

This code is based on the paper Exploiting Object Similarity in 3D Reconstruction.
C++
1
star
43

slow_flow

This code is based on the paper Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data.
C++
1
star