Attention-Based Point Cloud Sampling

Point cloud processing has emerged as a pivotal area of research in 3D computer vision, driven by the increasing demand for accurate and efficient representation of three-dimensional data in various applications, including autonomous driving, robotics, and virtual reality. Despite significant advancements, the task of effectively sampling and processing point clouds to maintain crucial geometric features while optimizing downstream task performance remains a substantial yet underexplored challenge. Traditional point cloud sampling methods have been widely used to address such challenges due to their simplicity and efficiency. However, they often fall short in adapting to varying tasks and maintaining a balance between preserving critical details and ensuring uniformity. Recent progress in learning-based sampling methods has shown significant promise in efficiently reducing point cloud size while achieving good performance on 3D vision tasks. Nevertheless, these generative-based methods primarily focus on creating new point clouds that approximate the original distribution, making it challenging to trace sampled points back to their original locations and discern learned patterns. Motivated by the strengths and limitations of both traditional and learning-based sampling methods, this dissertation proposes a series of innovative point cloud sampling methods—APES, SAMPS, and SAMBLE—that integrate task-oriented learning with mathematical statistics-based direct point selection. Each method builds upon the insights and innovations of its predecessors, progressively advancing the state of point cloud sampling techniques. The first method introduced in this dissertation is the Attention-based Point cloud Edge Sampling (APES). Inspired by the Canny edge detection algorithm used in image processing, APES adapts this concept to the 3D domain by utilizing attention-based mechanisms to identify and sample edge points in point clouds. The method pioneers the integration of task-oriented learning with mathematically traceable direct point selection. However, APES also highlights the challenge of maintaining shape uniformity while focusing on edge preservation, necessitating more sophisticated sampling strategies. Building on the foundation laid by APES, the Sparse Attention Map-based Point cloud Sampling (SAMPS) method addresses its predecessor’s limitations by introducing a sparse attention map that combines local and global information. This method achieves a more effective trade-off between sampling edge points and maintaining the global uniformity of the point cloud, showing significant improvements especially when only a limited number of points are sampled. Despite these advancements, SAMPS reveals the need for adaptive strategies tailored to the unique characteristics of different point clouds. The final method, Sparse Attention Map and Bin-based Learning (SAMBLE) method, represents a significant advancement by learning shape-specific sampling strategies. Building on the sparse attention map proposed in SAMPS, SAMBLE computes point-wise sampling scores and partitions points into bins to enhance discrimination among different point categories. By learning bin boundaries adaptively and determining bin sampling ratios with additional tokens during attention computation, SAMBLE tailors sampling strategies for each shape, leading to enhanced performance across various downstream tasks. Both quantitative and qualitative results from extensive experiments demonstrate that these methods contribute to developing more effective and adaptable point cloud sampling strategies, enhancing the overall quality of the sampled point cloud data and achieving better performance on various point cloud downstream tasks. The findings and methodologies presented in this dissertation lay the groundwork for future advancements in point cloud processing, paving the way for further enhanced methodologies and more efficient processing techniques that can potentially revolutionize how point cloud data is handled and utilized in diverse real-world applications.