MLOD: A multi-view 3D object detection based on robust feature fusion method
J Deng, K Czarnecki - 2019 IEEE intelligent transportation …, 2019 - ieeexplore.ieee.org
This paper presents Multi-view Labelling Object Detector (MLOD). The detector takes an
RGB image and a LIDAR point cloud as input and follows the two-stage object detection …
RGB image and a LIDAR point cloud as input and follows the two-stage object detection …
MVMM: Multiview multimodal 3-D object detection for autonomous driving
S Li, K Geng, G Yin, Z Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Object detection in 3-D space is a fundamental technology in the autonomous driving
system. Among the published 3-D object detection methods, the single-modal methods …
system. Among the published 3-D object detection methods, the single-modal methods …
3D-GIoU: 3D generalized intersection over union for object detection in point cloud
J Xu, Y Ma, S He, J Zhu - Sensors, 2019 - mdpi.com
Three-dimensional (3D) object detection is an important research in 3D computer vision with
significant applications in many fields, such as automatic driving, robotics, and human …
significant applications in many fields, such as automatic driving, robotics, and human …
Unsupervised object detection with lidar clues
Despite the importance of unsupervised object detection, to the best of our knowledge, there
is no previous work addressing this problem. One main issue, widely known to the …
is no previous work addressing this problem. One main issue, widely known to the …
Adversarial attacks on camera-lidar models for 3d car detection
Most autonomous vehicles (AVs) rely on LiDAR and RGB camera sensors for perception.
Using these point cloud and image data, perception models based on deep neural nets …
Using these point cloud and image data, perception models based on deep neural nets …
Center3d: Center-based monocular 3d object detection with joint depth understanding
Y Tang, S Dorn, C Savani - DAGM German Conference on Pattern …, 2020 - Springer
Localizing objects in 3D space and understanding their associated 3D properties is
challenging given only monocular RGB images. The situation is compounded by the loss of …
challenging given only monocular RGB images. The situation is compounded by the loss of …
Automatic augmentation and segmentation system for three-dimensional point cloud of pavement potholes by fusion convolution and transformer
J Dong, N Wang, H Fang, H Lu, D Ma, H Hu - Advanced Engineering …, 2024 - Elsevier
The regular three-dimensional (3D) detection of potholes is essential for the assessment of
pavement conditions. However, some problems associated with the segmentation of …
pavement conditions. However, some problems associated with the segmentation of …
Pseudo-image and sparse points: Vehicle detection with 2D LiDAR revisited by deep learning-based methods
Detecting and locating surrounding vehicles robustly and efficiently are essential
capabilities for autonomous vehicles. Existing solutions often rely on vision-based methods …
capabilities for autonomous vehicles. Existing solutions often rely on vision-based methods …
Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection
The pursuit of autonomous driving relies on developing perception systems capable of
making accurate, robust, and rapid decisions to interpret the driving environment effectively …
making accurate, robust, and rapid decisions to interpret the driving environment effectively …
Deep learning inspired object consolidation approaches using lidar data for autonomous driving: a review
Abstract Autonomous Driving Vehicle (ADV) services have become a prominent motif in
intelligent vehicle technology by adapting deep learning features. Automated driverless …
intelligent vehicle technology by adapting deep learning features. Automated driverless …