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Enhancing Rotation-Invariant 3D Learning with Global Pose Awareness and Attention Mechanisms

arXiv — cs.CV•Thursday, November 13, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
Recent advancements in rotation-invariant learning for 3D point clouds have highlighted a significant limitation: the loss of global pose information, which hampers the ability to distinguish between geometrically similar yet spatially distinct structures. This issue, identified in the latest research, leads to a phenomenon known as Wing-tip feature collapse, where symmetric components cannot be differentiated due to indistinguishable local geometries. To address this, the study proposes the Shadow-informed Pose Feature (SiPF), which enhances local rotation-invariant descriptors by incorporating a globally consistent reference point derived from a learned shared rotation. Additionally, the introduction of Rotation-invariant Attention Convolution (RIAttnConv) integrates SiPFs into the feature aggregation process, significantly enhancing the model's capacity to distinguish structurally similar components. These innovations are crucial for advancing applications in computer vision and rob…
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