Exploring Local and Global Consistent Correlation on Hypergraph for Rotation Invariant Point Cloud Analysis

Rotation invariant point cloud analysis is essential for many real-world applications where objects can appear in arbitrary orientations. Traditional local rotation-invariant methods rely on lossy region descriptors, limiting the global comprehension of 3D objects. Conversely, global features derived from pose alignment can capture complementary information. To leverage both local and global consistency for enhanced accuracy, we propose the Global-Local-Consistent Hypergraph Cross-Attention Network (GLC-HCAN). This framework includes the Global Consistent Feature (GCF) representation branch, the Local Consistent Feature (LCF) representation branch, and the Hypergraph Cross-Attention (HyperCA) network to model complex correlations through the global-local-consistent hypergraph representation learning. Specifically, the GCF branch employs a multi-pose grouping and aggregation strategy based on PCA for improved global comprehension. Simultaneously, the LCF branch uses local farthest reference point features to enhance local region descriptions. To capture high-order and complex global-local correlations, we construct hypergraphs that integrate both features, mutually enhancing and fusing the representations. The inductive HyperCA module leverages attention techniques to better utilize these high-order relations for comprehensive understanding. Consequently, GLC-HCAN offers an effective and robust rotation-invariant point cloud analysis network, suitable for object classification and shape retrieval tasks in SO(3). Experimental results on both synthetic and scanned point cloud datasets demonstrate that GLC-HCAN outperforms state-of-the-art methods.