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Autonomous Vision Blog

This is the blog of the Autonomous Vision Group at MPI-IS Tübingen and University of Tübingen. You can visit our blog at https://autonomousvision.github.io. Also check out our website to learn more about our research.

Overview

Creating a blog post follows the usual git workflow:

  1. clone repository:

    git clone https://github.com/autonomousvision/autonomousvision.github.io.git
    
  2. create new branch for your post:

    git branch my-post
    git checkout my-post
    
  3. work on branch / push my-post branch for collaboration

  4. rebase master on your branch and squash commits (note that all your commits to master will be visible in the git history):

    git checkout master
    git rebase -i my-post
    
  5. push master

    git push origin master
    
  6. delete your branch

    locally:

    git branch -d my-post
    

    and remotely if you pushed your branch in step 3:

    git push origin --delete my-post
    

Instructions for Authors

To write a new blog entry, first register yourself as an author in authors.yml. Here, you can also add your email address and links to your social media accounts etc.

You can then create a new blog post by adding a markdown or html file in the _posts folder. Please use the format YYYY-MM-DD-YOUR_TITLE.{md,html} for naming the file. You can then create a yaml header where you specify the author, the category of the post, tags, etc. For more information, take a look at existing posts and the Minimal Mistakes documentation.

If you want to include images or other assets, create a subfolder in the assets/posts folder with the same name as the filename of your blog post (without extension). You can simply reference your assets in your post using {{ site.url }}/assets/posts/YYYY-MM-DD-YOUR_TITLE/ followed by the filename of the corresponding asset. Make sure that you don't forget to include the {{ site.url }}! While the post while be rendered correctly without the {{ site.url }}, the images in the newsfeed will break if you don't include it.

Please keep in mind that all your commits to master will appear in the git history. To keep this history clean, it might make sense to edit your post in a separate (private) branch and then merge this branch into master.

Offline editing

When you do offline editing, you probably want to build the website offline for a preview. To this end, you first have to install Ruby and Jekyll. Then, you have to install the dependencies (called Gems) for the website:

bundle

Now, you are ready to build and serve the website using

 bundle exec jekyll serve

Sometimes Jekyll hiccups over character encoding. In this case, try

 LANG=en_US.UTF-8 LC_ALL=en_US.UTF-8 bundle exec jekyll serve

If you encounter GemNotFoundException, try to remove

BUNDLED WITH
    2.0.1

from Gemfile.lock.

This command will build the website and serve it at http://localhost:4000. When you save changes, the website will be automatically rebuilt in the background. Note, however, that changes to _config.yaml will not be tracked which means that you have to restart the jekyll server after configuration changes.

References

You can find more information here:

autonomousvision's Projects

campari icon campari

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

connecting_the_dots icon 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

data_aggregation icon data_aggregation

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

differentiable_volumetric_rendering icon differentiable_volumetric_rendering

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

factor-fields icon factor-fields

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

frequency_bias icon frequency_bias

Official code for "On the Frequency Bias of Generative Models", NeurIPS 2021

giraffe icon giraffe

This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

good icon good

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

graf icon graf

Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"

gta icon gta

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

king icon king

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

kitti360scripts icon kitti360scripts

This repository contains utility scripts for the KITTI-360 dataset.

mip-splatting icon mip-splatting

[CVPR'24 Oral] Mip-Splatting: Alias-free 3D Gaussian Splatting

monosdf icon monosdf

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

murf icon murf

[CVPR'24] MuRF: Multi-Baseline Radiance Fields

navsim icon navsim

NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking

neat icon neat

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

occupancy_flow icon occupancy_flow

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

occupancy_networks icon occupancy_networks

This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"

plant icon plant

[CoRL'22] PlanT: Explainable Planning Transformers via Object-Level Representations

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