Few-Shot Point Cloud Region Annotation with Human in the Loop

TL;DR

This paper introduces a human-in-the-loop framework for efficient point cloud annotation that reduces human effort while maintaining high-quality, flexible annotations through iterative propagation and few-shot learning.

Contribution

It presents a novel framework combining human-in-the-loop learning with few-shot fine-tuning to efficiently annotate large point cloud datasets with minimal human interaction.

Findings

Significantly reduces human annotation effort.

Maintains high-quality point cloud segmentation.

Supports multiple annotation granularities.

Abstract

We propose a point cloud annotation framework that employs human-in-loop learning to enable the creation of large point cloud datasets with per-point annotations. Sparse labels from a human annotator are iteratively propagated to generate a full segmentation of the network by fine-tuning a pre-trained model of an allied task via a few-shot learning paradigm. We show that the proposed framework significantly reduces the amount of human interaction needed in annotating point clouds, without sacrificing on the quality of the annotations. Our experiments also suggest the suitability of the framework in annotating large datasets by noting a reduction in human interaction as the number of full annotations completed by the system increases. Finally, we demonstrate the flexibility of the framework to support multiple different annotations of the same point cloud enabling the creation of datasets with different granularities of annotation.