S ² PU-Net: Sparse Semantic-Guided Progressive Point Cloud Upsampling for Indoor Scenes

Recent advancements in 3D scanning technology have spurred extensive research on indoor scene point clouds. However, point clouds captured by 3D sensors are often sparse, noisy, and irregular, posing significant challenges for downstream tasks. While learning-based upsampling methods have achieved promising results, they often struggle with large-scale indoor scenes due to the high computational cost of K-Nearest Neighbor (KNN) search and insufficient reconstruction of fine-grained structures. To address these issues, we propose S 2 PU-Net, a sparse semantic-guided progressive point cloud upsampling network for indoor scenes. Specifically, we introduce a progressive upsampling mechanism based on sparse tensors, where a progressive Point Generation Block is designed to generate and prune intermediate point clouds iteratively. By employing multiple generative deconvolutions and adaptive pruning with different coefficients, our method progressively reconstructs the target point cloud while preserving fine-grained local structures. Furthermore, we integrate semantic information into each layer of the hourglass encoder through a Sparse Semantic Embedding (SSE) module, enhancing point feature extraction. To further strengthen feature representation, we propose a Multi-Scale Sparse Semantic Embedding (MSSE) module, which refines the fused features across spatial and channel dimensions. Extensive experiments on multiple indoor scene datasets demonstrate that our method consistently outperforms state-of-the-art approaches across most evaluation metrics.