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Enhancing Few-Shot 3D Point Cloud Semantic Segmentation through Bidirectional Prototype Learning

Published: 06 March 2024 Publication History

Abstract

In recent years, significant strides have been made in point cloud semantic segmentation, which, however, are unspectacular when the training is deprived of sufficient densely-annotated samples, especially with the face of new classes unseen during the training. Given limited data and unacquainted categories, learning efficiency becomes of great concern to the overall segmentation outcome. To obtain improved segmentation performance under this few-shot training condition, we introduce a bidirectional learning method that allows mutual prototype learning between support set and query set. Specifically, we manage to realize enhanced efficiency by exploiting the support and query sets to a larger extent, effectively extracting information and generating prototypes in two opposite learning orientations. Refined by our method, models are able to achieve better performance in few-shot 3D semantic segmentation tasks without the need of further introducing more parameters that may lead to higher model complexity. To validate our method, we respectively test different models for 1-shot and 5-shot settings on the S3DIS [23] dataset. The remarkably improved IoU scores on unseen classes in the evaluation tests show the effectiveness of our proposed method.

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  • (2025)Few-Shot Segmentation of 3D Point Clouds Under Real-World Distributional Shifts in Railroad InfrastructureSensors10.3390/s2504107225:4(1072)Online publication date: 11-Feb-2025

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cover image ACM Other conferences
ICRAI '23: Proceedings of the 2023 9th International Conference on Robotics and Artificial Intelligence
November 2023
72 pages
ISBN:9798400708282
DOI:10.1145/3637843
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 06 March 2024

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  1. 3D point cloud
  2. few-shot learning
  3. prototype learning
  4. semantic segmentation

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  • (2025)Few-Shot Segmentation of 3D Point Clouds Under Real-World Distributional Shifts in Railroad InfrastructureSensors10.3390/s2504107225:4(1072)Online publication date: 11-Feb-2025

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