Efficient and lightweight long-range modeling for 3d point cloud classification and segmentation

3D point clouds, with their compact structural representation and rich geometric information, have become fundamental data sources for visual understanding tasks in computer vision, robotics, and intelligent systems.Despite extensive progress, many existing methods still place strong emphasis on local geometric modeling while exhibiting limited ability to capture global long-range contextual dependencies. Moreover, the increasing architectural complexity of modern models often leads to high computational cost and memory consumption. In this paper, we propose Point BiLSTM, an efficient and lightweight framework for 3D point cloud classification and segmentation. The core of the proposed method is a bidirectional long short-term memory (BiLSTM)-based sequencer module, which models long-range contextual dependencies among points with linear computational complexity, enabling effective global feature learning at a low cost. Considering the unordered nature of point clouds, we further propose a Mixed Sequence Soft Cross-Entropy Loss that jointly supervises fixed-order and randomly permuted point sequences during training. This design explicitly enhances robustness to permutation ambiguity and improves training stability. Extensive experiments conducted on three widely used benchmarks—ModelNet40, ScanObjectNN, and ShapeNet Part—demonstrate that Point BiLSTM achieves highly competitive performance. In particular, the proposed method attains the fastest inference speed on both idealized and real-world datasets, outperforming current state-of-the-art methods by 30.2% and 54.2%, respectively. In addition, Point BiLSTM significantly reduces computational complexity and memory consumption, providing an effective solution for efficient point cloud learning. Our code will be available at https://github.com/wendaodao04/PointBiLSTM.