UniComplete: Unified Multimodal High-Resolution 3D Shape Completion via Anchored Latent Sets

Existing shape completion methods accept a single input modality and, when built on dense voxel grids, deliver watertight surfaces only at low resolution. Latent vector sets decouple the latent from the grid, but their latent tokens come from input-dependent queries, lack of consistent correspondence between partial and complete shapes, and surface extraction still needs a dense field query. We present UniComplete, a framework for multimodal shape completion built on an anchored latent set: each token is bound to a fixed canonical 3D anchor, so it keeps the same identity and spatial reference across shapes, modalities, and completeness levels. An SDF VAE defines the shared latent space, and modality-specific encoders are distilled into it, so signed and unsigned distance fields, occupancy grids, point clouds, and depth maps share one decoder and one completion model. Completion is a posterior-mean rectified flow: a partial encoder regresses the posterior-mean estimate of the complete latent, and a rectified flow started from that estimate yields multiple plausible completions in a few steps. Sparse surface encoding and a coarse-to-fine Surface Gate confine computation to the surface at the input and the output, so cost scales with the surface rather than the volume. We further introduce SfMComplete, a benchmark pairing partial shapes reconstructed by structure-from-motion from rendered images with exact CAD ground truth across modalities and resolutions. UniComplete is competitive on 3D-EPN and PatchComplete, improves over compared baselines on SfMComplete at higher resolutions, and completes all five input modalities at SDF resolutions up to $512^3$.