RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
Transformers in 3d point clouds: A survey
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PointTPA uses serialization-based neighborhood grouping and a dynamic parameter projector to adapt network weights per scene patch, reaching 78.4% mIoU on ScanNet with under 2% added parameters.
citing papers explorer
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RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings
RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
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PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understanding
PointTPA uses serialization-based neighborhood grouping and a dynamic parameter projector to adapt network weights per scene patch, reaching 78.4% mIoU on ScanNet with under 2% added parameters.