Exploring Label Initialization for Weakly Supervised Als Point Cloud Semantic Segmentation

Abstract

Abstract. Although a number of emerging point-cloud semantic segmentation methods achieve state-of-the-art results, acquiring fully interpreted training data is a time-consuming and labor-intensive task. To reduce the burden of data annotation in training, semiand weakly supervised methods are proposed to address the situation of limited supervisory sources, achieving competitive results compared to full supervision schemes. However, given a fixed budget, the effective annotation of a few points is typically ignored, which is referred to as weak-label initialization in this study. In practice, random selection is typically adopted by default. Because weakly supervised methods largely rely on semantic information supplied by initial weak labels, this studies explores the influence of different weak-label initialization strategies. In addition to random initialization, we propose a feature-constrained framework to guide the selection of initial weak labels. A feature space of point clouds is first constructed by feature extraction and embedding. Then, we develop a density-biased strategy to annotate points by locating highly dense clustered regions, as significant information distinguishing semantic classes is often concentrated in such areas. Our method outperforms random initialization on ISPRS Vaihingen 3D data when only using sparse weak labels, achieving an overall accuracy of 78.06% using 1‰ of labels. However, only a minor increase is observed on the LASDU dataset. Additionally, the results show that initialization with category-wise uniformly distributed weak labels is more effective when incorporated using a weakly supervised method.

A survey on weakly supervised 3D point cloud semantic segmentation

“…Yao et al [26] created limitations by balancing the authenticity of pseudo labels and original labels. Using self-supervised learning and label propagation, Zhang et al [27] restricted and optimised the network; Cheng et al [28] added pseudo label and attentional feature constraints; Wang and Yao [30,33], Liu et al [31,43], Shi et al [35] and Lu et al [41] all incorporated consistency and pseudo label constraints. As a novel hybrid constraint, Kong et al [36] merged pseudo labelling with weakly supervised information.…”

“…[12, 14] captured a truncated point cloud based on a particular viewpoint to obtain the corresponding 2D ground truth segmentation map. Wang and Yao [33] design a new weak label initialisation framework based on feature‐constrained. It uses manifold learning to optimise the selection of initial weak annotations to retain more significant semantic data by projecting the extracted features to a more suitable feature space for combination.…”

“…[27] restricted and optimised the network; Cheng et al. [28] added pseudo label and attentional feature constraints; Wang and Yao [30, 33], Liu et al. [31, 43], Shi et al.…”