9 accepted papers
Group Relative Policy Optimization (GRPO) trains Chain-of-Thought reasoning with verifiable rewards, but estimating thought-level advantages without value functions often suffers from high variance. Although tree-style branching is used in practice to reduce the variance, it lacks a theoretical expl…
The corresponding author of this letter [1] is Lujia Wang, not Sukai Wang.
"For autonomous driving systems, the storage cost and transmission speed of the large-scale point clouds become an important bottleneck because of their large volume. In this paper, we propose a range image-based three-stage framework to compress the scanning LiDAR’s point clouds using the entropy m…
"We present a novel octree-based multi-level framework for large-scale point cloud compression, which can organize sparse and unstructured point clouds in a memory-efficient way. In this framework, we propose a new entropy model that explores the hierarchical dependency in an octree using the contex…
In autonomous vehicles or robots, point clouds from LiDAR can provide accurate depth information of objects compared with 2D images, but they also suffer a large volume of data, which is inconvenient for data storage or transmission. In this paper, we propose a Range image-based Point Cloud Compress…
End-to-end 3D object detection and tracking based on point clouds is receiving more and more attention in many robotics applications, such as autonomous driving. Compared with 2D images, 3D point clouds do not have enough texture information for data association. Thus, we propose an end-to-end point
In this letter, we propose a novel coding architecture for LiDAR point cloud sequences based on clustering and prediction neural networks. LiDAR point clouds are structured, which provides an opportunity to convert the 3D data to a 2D array, represented as range images. Thus, we cast the LiDAR point
Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filter or particle filter) to predict object locations in a time sequence, however, they are vulnerable to extreme motion con
All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors. Recently, with the rise of deep learning, end-to-end control for autonomous vehicles has been well studied. However, most