English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Semantic segmentation of point clouds plays a crucial role in the 3D scene interpretation. However, the majority of supervised learning methods needs a great number of annotated data for training an effective model, which requires labor-intensive and time-consuming annotation of 3D points. In this work, we presented a simple but practical framework that adopts a few-shot learning strategy to semantically segment 3D point clouds using merely sparse annotations. Major contributions lie in two major aspects: (1) A few-shot learning framework for semantic segmentation with sparse annotation is designed, which requires less manual labeling work than conventional methods. (2) A distribution calibration method is introduced to achieve transfer learning based on pre-trained models and requires no re-training of neural networks. The accuracy of semantic segmentation has been greatly boosted by adapting the model trained with a small number of annotated data to a wide range of data by synchronous transfer learning. The proposed method is tested by two groups of benchmarks, namely the combination of the TUM-MLS-2016 dataset and the Toronto-3D MLS dataset, serving for providing the pre-trained model and test dataset, and the combination of the DALES dataset as the base dataset and the ISPRS benchmark dataset as the test dataset. We have obtained impressive results reaching the same level as the results of the state-of-the-art method using dense annotation with an overall accuracy reaching 0.81 for segmenting eight semantic classes on the Toronto-3D MLS dataset, and an overall accuracy of 0.81 for segmenting nine semantic classes on the ISPRS benchmark dataset, proving the effectiveness of the proposed framework.

A Simple Framework of Few-Shot Learning Using Sparse Annotations for Semantic Segmentation of 3D Point Clouds