We propose APML, a differentiable and efficient loss for point cloud prediction tasks, approximating one-to-one matching using Sinkhorn iterations with adaptive temperature.

We present the first SIM(3)-equivariant model for generalizable shape completion, achieving state‑of‑the‑art results on synthetic and real scans.