AI RESEARCH
REVNET: Rotation-Equivariant Point Cloud Completion via Vector Neuron Anchor Transformer
arXiv CS.CV
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ArXi:2601.08558v2 Announce Type: replace Incomplete point clouds captured by 3D sensors often result in the loss of both geometric and semantic information. Most existing point cloud completion methods are built on rotation-variant frameworks trained with data in canonical poses, limiting their applicability in real-world scenarios. While data augmentation with random rotations can partially mitigate this issue, it significantly increases the learning burden and still fails to guarantee robust performance under arbitrary poses.