Few-Shot Meta-Learning on Point Cloud for Semantic Segmentation

TL;DR

This paper introduces a meta-learning approach for semantic segmentation of 3D point clouds, enabling construction robots to quickly adapt to new environments with minimal data, thereby enhancing environmental understanding.

Contribution

It pioneers the application of Model-Agnostic Meta-Learning (MAML) to 3D point cloud data and creates datasets for meta-learning in 3D scenes.

Findings

The method enables rapid adaptation to new environments with few samples.

Applying MAML improves generalization in 3D point cloud segmentation.

Experiments demonstrate effective environmental understanding for construction robots.

Abstract

The promotion of construction robots can solve the problem of human resource shortage and improve the quality of decoration. To help the construction robots obtain environmental information, we need to use 3D point cloud, which is widely used in robotics, autonomous driving, and so on. With a good understanding of environmental information, construction robots can work better. However, the dynamic changes of 3D point cloud data may bring difficulties for construction robots to understand environmental information, such as when construction robots renovate houses. The paper proposes a semantic segmentation method for point cloud based on meta-learning. The method includes a basic learning module and a meta-learning module. The basic learning module is responsible for learning data features and evaluating the model, while the meta-learning module is responsible for updating the parameters of the model and improving the model generalization ability. In our work, we pioneered the method of producing datasets for meta-learning in 3D scenes, as well as demonstrated that the Model-Agnostic Meta-Learning (MAML) algorithm can be applied to process 3D point cloud data. At the same time, experiments show that our method can allow the model to be quickly applied to new environments with a few samples. Our method has important applications.