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 …
HEDNet: A hierarchical encoder-decoder network for 3d object detection in point clouds
Abstract 3D object detection in point clouds is important for autonomous driving systems. A
primary challenge in 3D object detection stems from the sparse distribution of points within …
primary challenge in 3D object detection stems from the sparse distribution of points within …
VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection
CH Wang, HW Chen, Y Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Many LiDAR-based methods for detecting large objects, single-class object detection, or
under easy situations were claimed to perform well. However, due to their failure to exploit …
under easy situations were claimed to perform well. However, due to their failure to exploit …
Pillarnest: Embracing backbone scaling and pretraining for pillar-based 3d object detection
Pillar-based 3D object detectors mainly employ randomly initialized 2D convolution neural
network (ConvNet) for feature extraction and fail to enjoy the benefits from the backbone …
network (ConvNet) for feature extraction and fail to enjoy the benefits from the backbone …
Unleash the potential of image branch for cross-modal 3d object detection
To achieve reliable and precise scene understanding, autonomous vehicles typically
incorporate multiple sensing modalities to capitalize on their complementary attributes …
incorporate multiple sensing modalities to capitalize on their complementary attributes …
Multi-sem fusion: multimodal semantic fusion for 3D object detection
LIDAR and camera fusion techniques are promising for achieving 3-D object detection in
autonomous driving (AD). Most multimodal 3-D object detection frameworks integrate …
autonomous driving (AD). Most multimodal 3-D object detection frameworks integrate …
Dyfusion: Cross-attention 3d object detection with dynamic fusion
J Bi, H Wei, G Zhang, K Yang… - IEEE Latin America …, 2024 - ieeexplore.ieee.org
In the realm of autonomous driving, LiDAR and camera sensors play an indispensable role,
furnishing pivotal observational data for the critical task of precise 3D object detection …
furnishing pivotal observational data for the critical task of precise 3D object detection …
SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection
LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing
high-performing 3D object detectors usually build dense feature maps in the backbone …
high-performing 3D object detectors usually build dense feature maps in the backbone …
FS-3DSSN: an efficient few-shot learning for single-stage 3D object detection on point clouds
AK Tiwari, GK Sharma - The Visual Computer, 2024 - Springer
The current 3D object detection methods have achieved promising results for conventional
tasks to detect frequently occurring objects like cars, pedestrians and cyclists. However, they …
tasks to detect frequently occurring objects like cars, pedestrians and cyclists. However, they …
Cascade fusion of multi-modal and multi-source feature fusion by the attention for three-dimensional object detection
F Yu, J Lian, L Li, J Zhao - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
Three dimensional (3D) object detection using the camera and light detection and ranging
(LiDAR) fusion model has received much attention for meeting the environmental sensing …
(LiDAR) fusion model has received much attention for meeting the environmental sensing …