Frame points attention convolution for deep learning on point cloud.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Point clouds represent non-Euclidean, irregular data, posing challenges for standard spatial discrete convolution.
• Existing methods struggle with direct application of convolution to disordered point cloud structures.

Purpose of the Study:

• To introduce a novel 3D spatial convolution operator, frame points attention convolution (FPAC), for point cloud data.
• To address the difficulties in applying traditional convolution to irregular, disordered point cloud structures.

Main Methods:

• FPAC utilizes pre-defined frame points and an attention mechanism to quantify local point correlations.
• It generates spatially continuous filters by combining correlations with frame point weights, enabling dynamic weight calculation.
• The operator is reformulated for reduced dimensionality, enhancing training speed and decreasing memory usage.

Main Results:

• FPAC-based networks demonstrated competitive performance against state-of-the-art methods on common point cloud tasks.
• Experiments on widely used datasets validated the effectiveness of the proposed FPAC operator.
• Significant improvements in training speed and reduced memory consumption were observed.

Conclusions:

• FPAC offers an effective and efficient solution for 3D spatial convolution on point cloud data.
• The method provides a robust alternative to existing approaches for point cloud analysis.
• FPAC shows promise for advancing deep learning applications in 3D data processing.