MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

December 22, 2016 · Entered Twilight · 🏛 2018 IEEE Intelligent Vehicles Symposium (IV)

"Last commit was 8.0 years ago (≥5 year threshold)"

Evidence collected by the PWNC Scanner

Repo contents: .gitignore, .gitmodules, LICENSE, README.md, data, demo.py, docu, download_data.py, hypes, incl, licenses, predict_joint.py, requirements.txt, submodules, train.py

Authors Marvin Teichmann, Michael Weber, Marius Zoellner, Roberto Cipolla, Raquel Urtasun arXiv ID 1612.07695 Category cs.CV: Computer Vision Cross-listed cs.RO Citations 752 Venue 2018 IEEE Intelligent Vehicles Symposium (IV) Repository https://github.com/MarvinTeichmann/MultiNet ⭐ 556 Last Checked 1 month ago

Abstract

While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving. Towards this goal, we present an approach to joint classification, detection and semantic segmentation via a unified architecture where the encoder is shared amongst the three tasks. Our approach is very simple, can be trained end-to-end and performs extremely well in the challenging KITTI dataset, outperforming the state-of-the-art in the road segmentation task. Our approach is also very efficient, taking less than 100 ms to perform all tasks.