RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
Masked autoencoders for 3d point cloud self-supervised learning.World Scientific Annual Review of Artificial Intelligence, 1:2440001
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Invaria trains point cloud encoders with next-resolution prediction to learn scale and density invariant features, yielding higher mIoU on ScanNet under lower resolution and scaled objects while using a smaller model.
Synthesis4AD generates controllable synthetic 3D defects via MPAS and MLLM to achieve state-of-the-art performance on Real3D-AD, MulSen-AD, and real industrial datasets.
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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Invaria: Learning Scale and Density Invariance in Point Clouds via Next-Resolution Prediction
Invaria trains point cloud encoders with next-resolution prediction to learn scale and density invariant features, yielding higher mIoU on ScanNet under lower resolution and scaled objects while using a smaller model.
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Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection
Synthesis4AD generates controllable synthetic 3D defects via MPAS and MLLM to achieve state-of-the-art performance on Real3D-AD, MulSen-AD, and real industrial datasets.