98 citations · 168 across the 31 of their papers we have counts for
9 papers · 1 filter
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds
Le Hui, Lingpeng Wang, Mingmei Cheng +2
3D object tracking in point clouds is still a challenging problem due to the sparsity of LiDAR points in dynamic environments. In this work, we propose a Siamese voxel-to-BEV track…
Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration
Haobo Jiang, Yaqi Shen, Jin Xie +3
In this paper, by modeling the point cloud registration task as a Markov decision process, we propose an end-to-end deep model embedded with the cross-entropy method (CEM) for unsu…
UnDeepLIO: Unsupervised Deep Lidar-Inertial Odometry
Yiming Tu, Jin Xie
Extensive research efforts have been dedicated to deep learning based odometry. Nonetheless, few efforts are made on the unsupervised deep lidar odometry. In this paper, we design…
Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration
Haobo Jiang, Jin Xie, Jianjun Qian +1
Point cloud registration is a fundamental problem in 3D computer vision. In this paper, we cast point cloud registration into a planning problem in reinforcement learning, which ca…
SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering
Yifan Zhao, Le Hui, Jin Xie
Point clouds obtained from 3D sensors are usually sparse. Existing methods mainly focus on upsampling sparse point clouds in a supervised manner by using dense ground truth point c…
SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network
Mingmei Cheng, Le Hui, Jin Xie +1
Point cloud semantic segmentation is a crucial task in 3D scene understanding. Existing methods mainly focus on employing a large number of annotated labels for supervised semantic…
Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action Tasks
Haobo Jiang, Jin Xie, Jian Yang
Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped Double Q-learning, as an effective variant of Double Q-learning,…
MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation
Guangwei Gao, Guoan Xu, Yi Yu +3
In recent years, how to strike a good trade-off between accuracy and inference speed has become the core issue for real-time semantic segmentation applications, which plays a vital…
Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition
Le Hui, Mingmei Cheng, Jin Xie +1
Point cloud based retrieval for place recognition is still a challenging problem due to drastic appearance and illumination changes of scenes in changing environments. Existing dee…