3D object detection for autonomous driving: A comprehensive survey

J Mao, S Shi, X Wang, H Li - International Journal of Computer Vision, 2023 - Springer
Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Robustness-aware 3d object detection in autonomous driving: A review and outlook

Z Song, L Liu, F Jia, Y Luo, C Jia… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

Bevfusion: A simple and robust lidar-camera fusion framework

T Liang, H Xie, K Yu, Z Xia, Z Lin… - Advances in …, 2022 - proceedings.neurips.cc
Fusing the camera and LiDAR information has become a de-facto standard for 3D object
detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to …

Bevdet: High-performance multi-camera 3d object detection in bird-eye-view

J Huang, G Huang, Z Zhu, Y Ye, D Du - arXiv preprint arXiv:2112.11790, 2021 - arxiv.org
Autonomous driving perceives its surroundings for decision making, which is one of the most
complex scenarios in visual perception. The success of paradigm innovation in solving the …

Bevdet4d: Exploit temporal cues in multi-camera 3d object detection

J Huang, G Huang - arXiv preprint arXiv:2203.17054, 2022 - arxiv.org
Single frame data contains finite information which limits the performance of the existing
vision-based multi-camera 3D object detection paradigms. For fundamentally pushing the …

Logonet: Towards accurate 3d object detection with local-to-global cross-modal fusion

X Li, T Ma, Y Hou, B Shi, Y Yang… - Proceedings of the …, 2023 - openaccess.thecvf.com
LiDAR-camera fusion methods have shown impressive performance in 3D object detection.
Recent advanced multi-modal methods mainly perform global fusion, where image features …

Beverse: Unified perception and prediction in birds-eye-view for vision-centric autonomous driving

Y Zhang, Z Zhu, W Zheng, J Huang, G Huang… - arXiv preprint arXiv …, 2022 - arxiv.org
In this paper, we present BEVerse, a unified framework for 3D perception and prediction
based on multi-camera systems. Unlike existing studies focusing on the improvement of …

Monodtr: Monocular 3d object detection with depth-aware transformer

KC Huang, TH Wu, HT Su… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Monocular 3D object detection is an important yet challenging task in autonomous driving.
Some existing methods leverage depth information from an off-the-shelf depth estimator to …

MonoDETR: Depth-guided transformer for monocular 3D object detection

R Zhang, H Qiu, T Wang, Z Guo, Z Cui… - Proceedings of the …, 2023 - openaccess.thecvf.com
Monocular 3D object detection has long been a challenging task in autonomous driving.
Most existing methods follow conventional 2D detectors to first localize object centers, and …

Cross-modality knowledge distillation network for monocular 3d object detection

Y Hong, H Dai, Y Ding - European Conference on Computer Vision, 2022 - Springer
Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D
detection has brought significant improvement, eg, Pseudo-LiDAR methods. However, the …