3DISCO: 3D Open Vocab Instance Segmentation with Clean Objects via Gaussian Splatting

Segmenting 3D scenes into semantically meaningful instances is central to embodied perception. Leveraging high-quality 2D segmentations across multiple views is promising, but maintaining consistent identities across views, constraining Gaussians to lie within one object's boundaries, and assigning semantics based on highly informative views are challenging. We introduce 3DISCO, a 3D segmentation method based on Gaussian splatting that identifies objects using instance features learned via uncertainty-aware contrastive training. 3DISCO treats 2D masks as noisy labels, using predicted uncertainty to down-weight unreliable regions and guide the recovery of erroneous or missing annotations. To enable cleaner boundary delineation, 3DISCO prunes Gaussians that exhibit feature similarity to an instance but are inconsistent with that instance's 2D masks. 3DISCO then assigns semantics to each instance using features from only its most informative views, selected via a multimodal reranker, producing language-aligned 3D representations. Experiments on LERF and ScanNet show substantial improvements in semantic accuracy and boundary coherence.