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 …
Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy
D Fernandes, A Silva, R Névoa, C Simões… - Information …, 2021 - Elsevier
Autonomous vehicles are becoming central for the future of mobility, supported by advances
in deep learning techniques. The performance of aself-driving system is highly dependent …
in deep learning techniques. The performance of aself-driving system is highly dependent …
Voxel-based representation of 3D point clouds: Methods, applications, and its potential use in the construction industry
Point clouds acquired through laser scanning and stereo vision techniques have been
applied in a wide range of applications, proving to be optimal sources for mapping 3D urban …
applied in a wide range of applications, proving to be optimal sources for mapping 3D urban …
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 …
Arkitscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data
Scene understanding is an active research area. Commercial depth sensors, such as Kinect,
have enabled the release of several RGB-D datasets over the past few years which …
have enabled the release of several RGB-D datasets over the past few years which …
Pvt-ssd: Single-stage 3d object detector with point-voxel transformer
Recent Transformer-based 3D object detectors learn point cloud features either from point-
or voxel-based representations. However, the former requires time-consuming sampling …
or voxel-based representations. However, the former requires time-consuming sampling …
VP-Net: Voxels as points for 3-D object detection
The 3-D object detection with light detection and ranging (LiDAR) point clouds is a
challenging problem, which requires 3-D scene understanding, yet this task is critical to …
challenging problem, which requires 3-D scene understanding, yet this task is critical to …
Pointacc: Efficient point cloud accelerator
Deep learning on point clouds plays a vital role in a wide range of applications such as
autonomous driving and AR/VR. These applications interact with people in real time on …
autonomous driving and AR/VR. These applications interact with people in real time on …
Transformer3D-Det: Improving 3D object detection by vote refinement
Voting-based methods (eg, VoteNet) have achieved promising results for 3D object
detection. However, the simple voting operation in VoteNet may lead to less accurate voting …
detection. However, the simple voting operation in VoteNet may lead to less accurate voting …
A comprehensive review on 3D object detection and 6D pose estimation with deep learning
Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …