Weakly Supervised Point Cloud Semantic Segmentation via Artificial Oracle

Manual annotation of every point in a point cloud is a costly and labor-intensive process. While weakly super-vised point cloud semantic segmentation (WSPCSS) with sparse annotation shows promise, the limited information from initial sparse labels can place an upper bound on performance. As a new research direction for WSPCSS, we propose a novel Region Exploration via Artificial Labeling (REAL) framework. It leverages a foundational image model as an artificial oracle within the active learning context, eliminating the need for manual annotation by a human oracle. To integrate the 2D model into the 3D domain, we first introduce a Projection-based Point-to-Segment (PP2S) module, designed to enable prompt segmentation of 3D data without additional training. The REAL framework samples query points based on model predictions and requests annotations from PP2S, dynamically refining labels and improving model training. Furthermore, to overcome several challenges of employing an artificial model as an oracle, we formulate effective query sampling and label updating strategies. Our comprehensive experiments and comparisons demonstrate that the REAL framework significantly outperforms existing methods across various benchmarks. The code is available at https://github.com/jihun1998/AO.