ReGen3D: Generalizable Unified Representation Learning for 3D Understanding

Existing 3D understanding models are typically built on afixed geometric representation and often fail when the representationformat changes. Unlike domain generalization, which studies distribu-tion shift under a fixed input structure, we focus on representation shift,where identical 3D semantics are expressed through heterogeneous geo-metric encodings such as points, voxels, meshes, and 3D Gaussian prim-itives. As 3D representations continue to evolve beyond a predefinedset, this challenge is becoming increasingly important. We formalize thisproblem as Representation Generalization (RG), a new setting for unified3D learning. RG requires a model to share knowledge across representa-tions during training, remain effective when only a single representationis available at test time, and extend efficiently to unseen or newly emerg-ing representations. To this end, we propose ReGen3D, a unified frame-work with three modules: Unified Representation Tokenization (URT),which maps heterogeneous representations into a shared token space;Shared Memory Interaction (SMI), which enables cross-representationlearning during training while supporting inference from any single avail-able representation; and Efficient Representation Adapter (ERA), whichsupports lightweight adaptation to unseen representations without modi-fying the shared backbone. We further establish RepShift-3D for system-atic evaluation under the RG setting. Extensive experiments on classi-fication, segmentation, and reconstruction show that ReGen3D achievesstrong and robust performance across diverse representation scenarios.