Abstract
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.
Highlights
Point clouds, collections of points in 3D space, are characterized by their powerful abilities to represent position, size, shape and orientation of objects
The major contribu tions of this paper are as follows: 1) We introduce an active and incremental learning framework to effectively reduce the number of training samples required by deep neural networks for semantic segmentation of large airborne laser scanning (ALS) point clouds
The random se lection leads to an unstable model performance which is illustrated by the large standard deviation in the mIoU
Summary
Collections of points in 3D space, are characterized by their powerful abilities to represent position, size, shape and orientation of objects. Identi fying urban objects like buildings, trees, and bridges requires a huge amount of human effort. To reduce this time-consuming and tedious work, researchers put their efforts on investigating the potential of machine learning techniques to deal with point cloud semantic under standing automatically. A lot of models have been researched for the task of semantic point cloud segmentation, like random Forests (RF) (Chehata et al, 2009), Supported Vector Machine (SVM) (Lodha et al, 2006), Gaussian Mixture Model (GMM) (Weinmann et al, 2014), AdaBoost (Lodha et al, 2007) and Artificial Neural Networks (ANN) (Xu et al, 2014). The ground truth for the semantic point cloud segmentation requires pointwise labelling, which is very timeconsuming when done manually. Strategies should be pro posed to alleviate such manual annotation effort
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