论文标题
一台单击：准场景级别弱监督点云语义分段，并通过主动学习
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning
论文作者
论文摘要
依赖大量注释以实现领先的绩效严重限制了大规模点云语义细分的实用性。为了降低数据注释成本，制定了有效的标签方案，并有助于在弱监督策略下获得竞争成果。重新访问当前的弱标签表格，我们介绍了一台单击（OCOC），这是一个低成本但有益的准场景级标签，该标签封装了点级和场景级别的注释。提出了一个积极的弱监督框架，以通过涉及全球和地方观点的弱监督来利用稀缺标签。上下文约束是通过辅助场景分类任务施加的，分别基于全局功能嵌入和点的预测聚合，该任务仅限于模型预测，仅将模型预测限制为OCOC标签。此外，我们设计了一种上下文感知的伪标签策略，该策略有效地补充了点级的监督信号。最后，具有不确定性度量的主动学习方案 - 时间输出差异已集成以检查信息的样本并提供有关子云查询的指导，该查询有利于快速获得理想的OCOC注释并将标签成本降低到极低的程度。使用从机载，移动和地面平台收集的三个LIDAR基准测试的广泛的实验分析表明，我们提出的方法取得了非常有希望的结果，尽管有稀缺的标签。就F1的平均得分而言，它的表现大大优于真正的场景级别弱监督的方法，最高25 \％，并针对完整的监督计划取得了竞争成果。在陆生雷达数据集 - 语义3D上，使用标签的大约2 \ textpertthent千{}，我们的方法的平均F1得分为85.2 \％，与基线模型相比，它的平均F1得分增加了11.58 \％。
Reliance on vast annotations to achieve leading performance severely restricts the practicality of large-scale point cloud semantic segmentation. For the purpose of reducing data annotation costs, effective labeling schemes are developed and contribute to attaining competitive results under weak supervision strategy. Revisiting current weak label forms, we introduce One Class One Click (OCOC), a low cost yet informative quasi scene-level label, which encapsulates point-level and scene-level annotations. An active weakly supervised framework is proposed to leverage scarce labels by involving weak supervision from global and local perspectives. Contextual constraints are imposed by an auxiliary scene classification task, respectively based on global feature embedding and point-wise prediction aggregation, which restricts the model prediction merely to OCOC labels. Furthermore, we design a context-aware pseudo labeling strategy, which effectively supplement point-level supervisory signals. Finally, an active learning scheme with a uncertainty measure - temporal output discrepancy is integrated to examine informative samples and provides guidance on sub-clouds query, which is conducive to quickly attaining desirable OCOC annotations and reduces the labeling cost to an extremely low extent. Extensive experimental analysis using three LiDAR benchmarks collected from airborne, mobile and ground platforms demonstrates that our proposed method achieves very promising results though subject to scarce labels. It considerably outperforms genuine scene-level weakly supervised methods by up to 25\% in terms of average F1 score and achieves competitive results against full supervision schemes. On terrestrial LiDAR dataset - Semantics3D, using approximately 2\textpertenthousand{} of labels, our method achieves an average F1 score of 85.2\%, which increases by 11.58\% compared to the baseline model.
