3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds

Author:

Xiao Aoran1,Huang Jiaxing1,Xuan Weihao2,Ren Ruijie3,Liu Kangcheng1,Guan Dayan4,El Saddik Abdulmotaleb4,Lu Shijian1,Xing Eric4

Affiliation:

1. Nanyang Technological University

2. Waseda University

3. Technical University of Denmark

4. Mohamed bin Zayed University of Artificial Intelligence

Publisher

IEEE

Reference63 articles.

1. Vision meets robotics: The KITTI dataset

2. Deep fusionnet for point cloud semantic segmentation;zhang;European Conference on Computer Vision,0

3. Unsupervised domain adaptation by backpropagation;ganin;International Conference on Machine Learning,0

4. BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

5. LiDAR Snowfall Simulation for Robust 3D Object Detection

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1. TSG-Seg: Temporal-selective guidance for semi-supervised semantic segmentation of 3D LiDAR point clouds;ISPRS Journal of Photogrammetry and Remote Sensing;2024-10

2. Domain generalization for semantic segmentation: a survey;Artificial Intelligence Review;2024-08-12

3. SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

4. Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions;IEEE Robotics and Automation Letters;2024-06

5. SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation;2024 International Conference on 3D Vision (3DV);2024-03-18

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