GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds-Reference-Cited by-同舟云学术

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GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Zhang Zihui¹,Yang Bo¹,Wang Bing¹,Li Bo¹

Affiliation:

1. Shenzhen Research Institute, The Hong Kong Polytechnic University

Funder

Shenzhen Science and Technology Innovation Commission

Research Grants Council of Hong Kong

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203698.pdf?arnumber=10203698

Reference79 articles.

1. PCT: Point cloud transformer

2. DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation

3. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

4. Scribble-Supervised LiDAR Semantic Segmentation

5. Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models;ha;CoRL,2022

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1. Multi-Scale Classification and Contrastive Regularization: Weakly Supervised Large-Scale 3D Point Cloud Semantic Segmentation;Remote Sensing;2024-09-07

2. Point cloud semantic segmentation with adaptive spatial structure graph transformer;International Journal of Applied Earth Observation and Geoinformation;2024-09

3. Semantic segmentation of point clouds of ancient buildings based on weak supervision;Heritage Science;2024-07-09

4. Segmentation of 3D Point Clouds of Heritage Buildings Using Edge Detection and Supervoxel-Based Topology;Sensors;2024-07-06

5. Maize stem–leaf segmentation framework based on deformable point clouds;ISPRS Journal of Photogrammetry and Remote Sensing;2024-05

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