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NeuRAD: Neural Rendering for Autonomous Driving

Tonderski, Adam LU orcid ; Lindström, Carl ; Hess, Georg ; Ljungbergh, William ; Svensson, Lennart and Petersson, Christoffer (2024) 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) p.14895-14904
Abstract
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent meth-ods show NeRFs' potential for closed-loop simulation, en-abling testing of AD systems, and as an advanced training data augmentation technique. However, existing meth-ods often require long training times, dense semantic su-pervision, or lack generalizability. This, in turn, hinders the application of NeRFs for AD at scale. In this paper, we propose NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design, extensive sensor modeling for both cam-era and lidar - including rolling shutter, beam divergence and ray dropping - and is applicable to multiple datasets out of the box.... (More)
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent meth-ods show NeRFs' potential for closed-loop simulation, en-abling testing of AD systems, and as an advanced training data augmentation technique. However, existing meth-ods often require long training times, dense semantic su-pervision, or lack generalizability. This, in turn, hinders the application of NeRFs for AD at scale. In this paper, we propose NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design, extensive sensor modeling for both cam-era and lidar - including rolling shutter, beam divergence and ray dropping - and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets, achieving state-of-the-art performance across the board. To encourage further development, we openly release the NeuRAD source code. (Less)
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author
; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
pages
14895 - 14904
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
conference dates
2024-06-16 - 2024-06-22
external identifiers
  • scopus:85190896037
ISBN
979-8-3503-5300-6
DOI
10.1109/CVPR52733.2024.01411
language
English
LU publication?
yes
id
dcda7743-11f9-4720-a0a5-110371f106c9
date added to LUP
2025-01-31 10:23:59
date last changed
2025-10-14 09:27:20
@inproceedings{dcda7743-11f9-4720-a0a5-110371f106c9,
  abstract     = {{Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent meth-ods show NeRFs' potential for closed-loop simulation, en-abling testing of AD systems, and as an advanced training data augmentation technique. However, existing meth-ods often require long training times, dense semantic su-pervision, or lack generalizability. This, in turn, hinders the application of NeRFs for AD at scale. In this paper, we propose NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design, extensive sensor modeling for both cam-era and lidar - including rolling shutter, beam divergence and ray dropping - and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets, achieving state-of-the-art performance across the board. To encourage further development, we openly release the NeuRAD source code.}},
  author       = {{Tonderski, Adam and Lindström, Carl and Hess, Georg and Ljungbergh, William and Svensson, Lennart and Petersson, Christoffer}},
  booktitle    = {{2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  isbn         = {{979-8-3503-5300-6}},
  language     = {{eng}},
  month        = {{06}},
  pages        = {{14895--14904}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{NeuRAD: Neural Rendering for Autonomous Driving}},
  url          = {{http://dx.doi.org/10.1109/CVPR52733.2024.01411}},
  doi          = {{10.1109/CVPR52733.2024.01411}},
  year         = {{2024}},
}