Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles

Author:

Nunes Lucas1ORCID,Chen Xieyuanli1ORCID,Marcuzzi Rodrigo1ORCID,Osep Aljosa2,Leal-Taixe Laura2ORCID,Stachniss Cyrill1ORCID,Behley Jens1ORCID

Affiliation:

1. University of Bonn, Bonn, Germany

2. Technical University of Munich, Munich, Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Reference43 articles.

1. Barlow twins: Self-supervised learning via redundancy reduction;zbontar;Proc Int Conf Mach Learn,0

2. Hdnet: Exploiting HD maps for 3D object detection;yang;Proc Conf Robot Learn,0

3. Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

4. Scalability in Perception for Autonomous Driving: Waymo Open Dataset

5. Deep inside convolutional networks: Visualising image classification models and saliency maps;simonyan;Proc Int Conf Learn Representations,0

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1. Fast and Robust Normal Estimation for Sparse LiDAR Scans;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Kernel-Based Attention Network for Point Cloud Compression;2023 IEEE International Conference on Robotics and Biomimetics (ROBIO);2023-12-04

3. ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

4. Exploiting the Joint Potential of Instance Segmentation and Semantic Segmentation in Autonomous Driving;2023 International Conference for Advancement in Technology (ICONAT);2023-01-24

5. Synthetic Data Generation on Dynamic Industrial Environment for Object Detection, Tracking, and Segmentation CNNs;Technological Innovation for Connected Cyber Physical Spaces;2023

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