Attention‐Based Fusion of Pretrained MaskPoint and Point‐MAE Representations for Enhanced Point Cloud Classification

ABSTRACT This paper proposes a novel attention‐based fusion framework that integrates two self‐supervised learning models, the Point‐MAE and MaskPoint methods, to improve 3D point cloud classification. Each model is pretrained on a carefully selected dataset to optimize downstream performance. Their pretrained encoders are then fused using two attention strategies, namely multi‐layer attention and cross‐attention, to capture complementary features from both networks. A comparative evaluation of their original classification heads revealed that the Point‐MAE architecture, featuring a three‐layer MLP with batch normalization and dropout, consistently outperforms the MaskPoint design and was thus adopted in the proposed framework. Extensive experiments on both synthetic and real‐world benchmarks, including ModelNet40 and multiple variants of ScanObjectNN, demonstrate that attention‐based fusion improves classification robustness in challenging scenarios, with cross‐attention consistently achieving the highest accuracy among the evaluated fusion strategies. Controlled baseline experiments further confirm that these gains stem from meaningful feature interaction. The proposed approach demonstrates that attention‐guided fusion of self‐supervised point cloud representations is an effective and flexible strategy for enhancing downstream 3D recognition performance.