# ViewPCL: a point cloud based active learning method for multi-view segmentation

Source: [ViewPCL: a point cloud based active learning method for multi-view segmentation](<https://www.sota2.com/research/paper/68fa796a1e209131cea7d7e6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:57:49.425Z

Authors: Christian Hilaire, Sima Didari

Published: 2025-06-16T02:16:17.000Z

## Abstract

We propose a novel active learning framework for multi-view semantic segmentation. This framework relies on a new score that measures the discrepancy between point cloud distributions generated from the extra geometrical information derived from the model's prediction across different views. Our approach results in a data efficient and explainable active learning method. The source code is available at https://github.com/chilai235/viewpclAL.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2506.13043>)

[PDF](<https://arxiv.org/pdf/2506.13043.pdf>)