CanonAE: Scalable Pose Synchronization for 3D Shape Representation Learning

Many 3D generative pipelines rely on autoencoders (AEs) whose reconstruction quality depends on training-shape orientations. Pose variation complicates learning, while semantic alignment need not optimize geometric representation. We propose CanonAE, which learns canonical orientations from collection geometry and the chosen AE’s reconstruction objective without prescribed semantic frames. Geometric pose synchronization initializes consistent poses, followed by joint pose and AE refinement. Our analysis characterizes local pose sensitivity to changes in matching losses and gives conditions for small subsequent AE-driven pose adjustments. Motivated by this analysis, our hierarchical, symmetry-aware synchronization retains multiple alignment hypotheses and scales to hundreds of thousands of shapes. Experiments on Thingi10K, CanoVerse, and protein densities show consistently lower test reconstruction error across two AE backbones. Ablations attribute most gains to synchronization, with modest benefits from joint refinement. These results support scalable geometric canonicalization for 3D representation learning and motivate its use in latent-space generative modeling.