ReMAP-DP: Reprojected Multi-view Aligned PointMaps for Diffusion Policy

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• ReMAP-DP achieves a 59.3% average success rate on RoboTwin 2.0, outperforming the DP3 baseline by +6.6%.result 95%
• ReMAP-DP yields a 28% improvement over DP3 on the geometrically challenging Stack Cube task in ManiSkill 3.result 95%
• ReMAP-DP exhibits remarkable real-world robustness and executes high-precision manipulations with superior data efficiency from only a handful of demonstrations.result 85%
• ReMAP-DP synergizes standardized perspective reprojection with a structure-aware dual-stream diffusion policy.method 90%
• The dual-stream architecture leverages learnable modality embeddings to fuse frozen semantic features and explicit geometric descriptors.technical 90%
• Existing 3D integration methods struggle due to structural irregularity of sparse point clouds and geometric distortion from multi-view orthographic rendering.limitation 85%
• Generalist robot policies built upon 2D visual representations inherently lack explicit 3D spatial awareness required for high-precision tasks.limitation 80%
• The method ensures precise implicit patch-level alignment through its architectural design.technical 80%

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