3D object detection for autonomous driving: A comprehensive survey
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 …
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
In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …
accurately assessing the state of the surrounding environment, thereby enabling informed …
Bevfusion: A simple and robust lidar-camera fusion framework
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 …
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 …
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 …
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
LiDAR-camera fusion methods have shown impressive performance in 3D object detection.
Recent advanced multi-modal methods mainly perform global fusion, where image features …
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
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 …
based on multi-camera systems. Unlike existing studies focusing on the improvement of …
Monodtr: Monocular 3d object detection with depth-aware transformer
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 …
Some existing methods leverage depth information from an off-the-shelf depth estimator to …
MonoDETR: Depth-guided transformer for monocular 3D object detection
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 …
Most existing methods follow conventional 2D detectors to first localize object centers, and …
Mononerd: Nerf-like representations for monocular 3d object detection
In the field of monocular 3D detection, it is common practice to utilize scene geometric clues
to enhance the detector's performance. However, many existing works adopt these clues …
to enhance the detector's performance. However, many existing works adopt these clues …