Supervised training of a deep neural network for semantic segmentation of point clouds requires a large amount of labelled data. Nowadays, it is easy to acquire a huge number of points with high density in large-scale areas using current LiDAR and photogrammetric techniques. However it is extremely time-consuming to manually label point clouds for model training. In this paper, we propose an active and incremental learning strategy to iteratively query informative point cloud data for manual annotation and the model is continuously trained to adapt to the newly labelled samples in each iteration. We evaluate the data informativeness step by step and effectively and incrementally enrich the model knowledge. The data informativeness is estimated by two data dependent uncertainty metrics (point entropy and segment entropy) and one model dependent metric (mutual information). The proposed methods are tested on two datasets. The results indicate the proposed uncertainty metrics can enrich current model knowledge by selecting informative samples, such as considering points with difficult class labels and choosing target objects with various geometries in the labelled training pool. Compared to random selection, our metrics provide valuable information to significantly reduce the labelled training samples. In contrast with training from scratch, the incremental fine-tuning strategy significantly save the training time.

在深度神经网络的监督训练中对点云进行语义分割需要大量的标记数据。如今，使用当前的LiDAR和摄影测量技术可以轻松在大规模区域中获取大量的高密度点。但是，手动标记点云以进行模型训练非常耗时。在本文中，我们提出了一种主动和增量学习策略，以迭代方式查询信息性点云数据以进行手动注释，并不断训练模型以适应每次迭代中新标记的样本。我们逐步评估数据的信息性，并有效，逐步丰富模型知识。数据的信息量由两个数据相关的不确定性度量（点熵和分段熵）和一个模型相关的度量（互信息）估计。所提出的方法在两个数据集上进行了测试。结果表明，所提出的不确定性度量可以通过选择信息样本来丰富当前的模型知识，例如考虑具有困难类别标签的点以及在标记的训练池中选择具有各种几何形状的目标对象。与随机选择相比，我们的指标提供了有价值的信息，可大大减少标记的训练样本。与从头开始训练相比，渐进式微调策略显着节省了训练时间。结果表明，所提出的不确定性度量可以通过选择信息样本来丰富当前的模型知识，例如考虑具有困难类别标签的点以及在标记的训练池中选择具有各种几何形状的目标对象。与随机选择相比，我们的指标提供了有价值的信息，可大大减少标记的训练样本。与从头开始训练相比，渐进式微调策略显着节省了训练时间。结果表明，所提出的不确定性度量可以通过选择信息样本来丰富当前的模型知识，例如考虑具有困难类别标签的点以及在标记的训练池中选择具有各种几何形状的目标对象。与随机选择相比，我们的指标提供了有价值的信息，可大大减少标记的训练样本。与从头开始训练相比，渐进式微调策略显着节省了训练时间。