Point clouds, essential for 3D perception, have gained significant attention in applications like computer vision, recognition, and human–computer interaction. They provide detailed object descriptions through high-resolution data captured by LiDAR or depth cameras. However, research shows that merely increasing the number of points in point clouds does not always improve task performance proportionally. With the increase in data resolution, the complexity of data processing rises significantly due to the discreteness and disorder of point clouds. This phenomenon not only may reduce the accuracy and robustness of downstream tasks but also, while high-resolution point cloud data provides richer information, it can introduce more noise and redundant data, thereby increasing the difficulty of processing and analysis. Therefore, how to reduce the point cloud data size while maintaining downstream task performance has become one of the key challenges in current research. Point cloud sampling, as a crucial technique for reducing data volume and enhancing data quality, aims to eliminate redundant or noisy points during the data processing stage, thereby reducing the point cloud size while preserving the integrity of geometric and structural information, ensuring task accuracy and reliability.
Common sampling techniques can be broadly classified into task-agnostic and task-oriented approaches. Task-agnostic algorithms, such as Farthest Point Sampling (FPS) and Random Sampling (RS), simplify raw point clouds using fixed sampling rules without considering downstream tasks, resulting in subsets that often exhibit suboptimal spatial distributions across different tasks. In contrast, task-oriented sampling methods use independent deep sampling modules that are decoupled from downstream tasks. For instance, with the advancement of deep learning techniques, methods such as SampleNet, LighTN, and APSNet have leveraged various neural network architectures—including multi-layer perceptrons, Transformers, and LSTMs—to improve the learning capability of sampling networks. MOPS-Net, on the other hand, frames the sampling problem as a matrix optimization task, learning a differentiable sampling matrix that is applied to the input point cloud to extract the sampled points. These methods typically rely on end-to-end training with pre-trained task networks and task-specific loss functions, enabling adaptive adjustment of point distributions according to downstream task requirements. However, these task-oriented methods treat individual points as the primary unit of importance for fine-grained sampling, and as a result, they often overlook the structural context of the point cloud. In contrast, our approach introduces a novel fusion of human visual perception-inspired mechanisms—global and local saliency cues—into the task-oriented sampling process. By considering both global and local structures, our method offers a more holistic view of the point cloud, effectively overcoming the limitations found in prior work and advancing point cloud sampling techniques.
The Human Visual System (HVS) inherently operates in a 3D space, which is crucial for human information acquisition. For machine vision systems, accurate 3D environment perception is vital for achieving human-like visual understanding and interaction. This capability enhances system performance and drives advancements in various intelligent applications. Regarding human visual attention mechanisms, the HVS tends to capture the information of 3D objects from abstract to detailed levels. For example, in cognitive psychology research , Navon pointed out that the HVS processed visual scenes hierarchically from top to bottom. They conceptualized the scene as a hierarchical structure composed of interrelated sub-scenes, where global features were perceived before local details within an observer’s effective visual span. Inspired by the observation, we assume that for sampling tasks, prioritizing the identification of critical regions and then narrowing down to local features have the potential to preserve more detailed structural features and produce more visually appealing results.
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