REVISABLE ONLINE 3D SCENE GRAPH GENERATION

Online 3D semantic scene graph generation must assign frame-level detections to persistent object nodes while observations are partial and noisy. Once made, these assignments and the resulting relation endpoints are usually fixed, so early false merges and splits persist throughout processing. We introduce Revisable 3D Scene Graph Generation (ReSGG), an online setting that allows historical observation-to-node assignments to change as new views provide evidence. We propose SGReviser, a training-free back end for existing scene graph front ends. SGReviser detects local association conflicts and compares joint and separate surface reconstructions using leave-one-frame-out geometric evidence. It converts the resulting signed preferences into an observation partition with a constrained correlation-clustering resolver, abstaining when the evidence is inconclusive. Because relation records retain their observation-level endpoints, graph edges can be repaired after repartitioning without re-predicting predicates. On 3DSSG, SGReviser improves relation Recall@1 by 16.4 points for FROSS and 13.0 points for NoPA; on ReplicaSSG, it improves the corresponding scores by 4.6 and 5.7 points. The method operates online with modest latency and requires no task-specific training, demonstrating that revising historical associations can substantially improve online 3D scene graph quality.