Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series-Reference-Cited by-同舟云学术

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Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series

Published:2023-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
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Author:

Kreutz Thomas¹,Muhlhauser Max¹,Guinea Alejandro Sanchez¹

Affiliation:

1. Technical University,Telekooperation Lab,Darmstadt

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10030081/10030084/10030114.pdf?arnumber=10030114

Reference38 articles.

1. Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs

2. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking;hurtado;ICRA,2022

3. An image is worth 16x16 words: Transformers for image recognition at scale;dosovitskiy,2020

4. Unsupervised representation learning for time series with temporal neighborhood coding;tonekaboni,2021

5. Unsupervised scalable representation learning for multivariate time series;franceschi;Advances in neural information processing systems,2019

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Joint Scene Flow Estimation and Moving Object Segmentation on Rotational LiDAR Data;IEEE Transactions on Intelligent Transportation Systems;2024

2. Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram;Computers, Materials & Continua;2024

3. MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation With Bird's Eye View Based Appearance and Motion Features;IEEE Robotics and Automation Letters;2023-12

4. InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

5. Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation;IEEE Robotics and Automation Letters;2023-08

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