EvObj: Learning Evolving Object-centric Representations for 3D Instance Segmentation without Scene Supervision

We introduce EVObject for unsupervised 3D instance segmentation that bridges the geometric domain gap between synthetic pretraining data and real-world point clouds. Current methods suffer from structural discrepancies when transferring object priors from synthetic datasets (e.g., ShapeNet) to real scans (e.g., ScanNet), particularly due to morphological variations and occlusion artifacts. To address this, EVObject integrates two innovative modules: (1) An object discerning module that dynamically refines object candidates, enabling continuous adaptation of object priors to target domains; and (2) An object completion module that reconstructs partial geometries before discovering object. We conduct extensive experiments on two real-world datasets and one synthetic dataset, demonstrating superior 3D object segmentation performance over all baselines while achieving state-of-the-art results.