A survey on deep-learning-based lidar 3d object detection for autonomous driving
LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast
decision-making when driving. The sensor is used in the perception system, especially …
decision-making when driving. The sensor is used in the perception system, especially …
Semantic-aware video compression for automotive cameras
Assisted and automated driving functions in vehicles exploit sensor data to build situational
awareness, however, the data amount required by these functions might exceed the …
awareness, however, the data amount required by these functions might exceed the …
Weight-based distributed formation control for Networked marine surface vehicles with Hybrid communication channel Deception Attacks
C Zhu, M Wang, B Huang - IEEE Transactions on Industrial …, 2024 - ieeexplore.ieee.org
The control interaction in networked marine surface vehicles (NMSVs) mainly involves three
communication channels, ie, vehicle-to-vehicle, sensor-to-controller, and controller-to …
communication channels, ie, vehicle-to-vehicle, sensor-to-controller, and controller-to …
Domain-generalized robotic picking via contrastive learning-based 6-d pose estimation
J Liu, W Sun, H Yang, C Liu, X Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Vision-guided robotic picking in 3-D space is a key technology for industrial automation and
intelligent manufacturing. However, existing methods rely on labeled real-world data for …
intelligent manufacturing. However, existing methods rely on labeled real-world data for …
Real-time LiDAR point-cloud moving object segmentation for autonomous driving
X Xie, H Wei, Y Yang - Sensors, 2023 - mdpi.com
The key to autonomous navigation in unmanned systems is the ability to recognize static
and moving objects in the environment and to support the task of predicting the future state …
and moving objects in the environment and to support the task of predicting the future state …
RRGA-Net: Robust Point Cloud Registration Based on Graph Convolutional Attention
J Qian, D Tang - Sensors, 2023 - mdpi.com
The problem of registering point clouds in scenarios with low overlap is explored in this
study. Previous methodologies depended on having a sufficient number of repeatable …
study. Previous methodologies depended on having a sufficient number of repeatable …
Patch-Wise LiDAR Point Cloud Geometry Compression Based on Autoencoder
R Huang, M Wang - International Conference on Image and Graphics, 2023 - Springer
Point cloud compression plays a critical role in efficient point cloud storage and
transmission. This paper focuses on the lossy geometric compression of LiDAR point clouds …
transmission. This paper focuses on the lossy geometric compression of LiDAR point clouds …
msLPCC: A Multimodal-Driven Scalable Framework for Deep LiDAR Point Cloud Compression
M Wang, R Huang, H Dong, D Lin, Y Song… - Proceedings of the AAAI …, 2024 - ojs.aaai.org
LiDAR sensors are widely used in autonomous driving, and the growing storage and
transmission demands have made LiDAR point cloud compression (LPCC) a hot research …
transmission demands have made LiDAR point cloud compression (LPCC) a hot research …
Intelligent Point Cloud Processing, Sensing, and Understanding
Point clouds are considered one of the fundamental pillars for representing the 3D digital
landscape [1], despite the irregular topology between discrete data points. Recent advances …
landscape [1], despite the irregular topology between discrete data points. Recent advances …
Dependence-Based Coarse-to-Fine Approach for Reducing Distortion Accumulation in G-PCC Attribute Compression
T Guo, H Yuan, R Hamzaoui, X Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Geometry-based point cloud compression (G-PCC) is a state-of-the-art point cloud
compression standard. While G-PCC achieves excellent performance, its reliance on the …
compression standard. While G-PCC achieves excellent performance, its reliance on the …