Cascaded discrete diffusion generates CAD command sequences with absorbing transitions and parameters with Gaussian, scale-invariant, and prior-preserving kernels, outperforming autoregressive and continuous diffusion baselines on the DeepCAD dataset.
Pointnet: Deep learning on point sets for 3d classification and segmentation
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Text-to-CAD retrieval is introduced as a cross-modal task with a baseline that learns joint embeddings from CAD construction sequences, point clouds, and text queries via a masked feature decoder.
Trajectory consistency training, smoothness regularization, and higher-order integration for flow matching policies deliver 60-70% success on long-horizon real-robot tasks where baselines achieve 0%.
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Computer-Aided Design Generation by Cascaded Discrete Diffusion Model
Cascaded discrete diffusion generates CAD command sequences with absorbing transitions and parameters with Gaussian, scale-invariant, and prior-preserving kernels, outperforming autoregressive and continuous diffusion baselines on the DeepCAD dataset.
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Text-to-CAD Retrieval: a Strong Baseline
Text-to-CAD retrieval is introduced as a cross-modal task with a baseline that learns joint embeddings from CAD construction sequences, point clouds, and text queries via a masked feature decoder.
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Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning
Trajectory consistency training, smoothness regularization, and higher-order integration for flow matching policies deliver 60-70% success on long-horizon real-robot tasks where baselines achieve 0%.