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Zhixuan Sun

2 accepted papers

2026

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

CVPR 2026

We introduce EvObj 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 sca

Cited by 0SourcecodeScholar
2026

FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation

ICML 2026poster

We address the challenging task of 3D object segmentation in complex scene point clouds without relying on any scene-level human annotations during training. Existing methods are typically constrained to identifying simple objects, primarily due to insufficient object priors in the learning process.…

Cited by 0SourceScholar