Attention Mechanism for Point Cloud Processing: A Comprehensive Review

Point clouds are collections of 3D points that hold valuable geographical information. However, applying traditional image processing techniques to point clouds can be challenging due to their irregular distribution and complexity. To overcome this obstacle, researchers proposed the use of attention mechanisms, which learn to focus on relevant parts of the input data when making decisions.
In point cloud processing, attention mechanisms are used to extract relationships between points and compute the importance of each point in relation to its neighbors. The attention mechanism first voxelizes the point cloud into smaller parts and then computes the scaled axis-aligned bounding box for each part. This creates a ground truth graph with an associated distance matrix, which is used for supervision in each layer.
The hop attention coefficient indicates how important a particular part is to its neighbors. To compute this value, the mechanism first predicts the hop distance between points and then multiplies it by the edge features between them. This embedding of the low-level geometric distance into high-level contextual features allows the attention mechanism to capture complex relationships between points.
In summary, attention mechanisms are a powerful tool for processing point clouds by learning to focus on relevant parts of the input data. By combining this technique with other neural network architectures, researchers have made significant progress in understanding and analyzing 3D point cloud data.