3D Instance Segmentation

3D instance segmentation assigns scene elements to individual objects, often alongside semantic labels, so robots and mapping systems can reason about cluttered or incomplete environments. Common approaches use point-cloud networks with query-based mask decoders or superpoint clustering, while newer pipelines lift 2D foundation-model masks across views or learn object identities in 3D Gaussian scenes. Indoor benchmarks such as ScanNet and ScanNet200, outdoor driving datasets such as SemanticKITTI and nuScenes, and domain-specific forest and biomedical data test these methods in different settings. A central challenge is balancing accurate, consistent object boundaries and open-vocabulary generalization with limited labels, occlusion, and the speed and memory demands of large scenes.