Active self-training for weakly supervised 3D scene semantic segmentation

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Gengxin Liu, Oliver van Kaick, Hui Huang, Ruizhen Hu
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引用次数: 0

Abstract

Since the preparation of labeled data for training semantic segmentation networks of point clouds is a time-consuming process, weakly supervised approaches have been introduced to learn from only a small fraction of data. These methods are typically based on learning with contrastive losses while automatically deriving per-point pseudo-labels from a sparse set of user-annotated labels. In this paper, our key observation is that the selection of which samples to annotate is as important as how these samples are used for training. Thus, we introduce a method for weakly supervised segmentation of 3D scenes that combines self-training with active learning. Active learning selects points for annotation that are likely to result in improvements to the trained model, while self-training makes efficient use of the user-provided labels for learning the model. We demonstrate that our approach leads to an effective method that provides improvements in scene segmentation over previous work and baselines, while requiring only a few user annotations.

Abstract Image

弱监督三维场景语义分割的主动自我训练
由于准备用于训练点云语义分割网络的标记数据是一个耗时的过程,因此引入了弱监督方法,只从一小部分数据中学习。这些方法通常基于对比损失学习,同时从稀疏的用户注释标签集中自动推导出每个点的伪标签。在本文中,我们的主要观点是,选择注释哪些样本与如何使用这些样本进行训练同样重要。因此,我们引入了一种结合自我训练和主动学习的弱监督三维场景分割方法。主动学习选择有可能改进训练模型的点进行标注,而自我训练则有效利用用户提供的标签来学习模型。我们证明,我们的方法是一种有效的方法,与以前的工作和基线相比,它在场景分割方面有所改进,同时只需要少量用户注释。
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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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