ScanNet-SG: A Large-Scale Dataset for 3D Scene Graph Alignment

3D scene graph alignment establishes correspondences between graphs built from partially overlapping observations, enabling robots to maintain consistent object-level representations across time and agents. It arises in two complementary settings: frame-to-scan (F2S), for aligning partial observations to a global map, and subscan-to-subscan (S2S), for aligning independently built submaps. Existing datasets support only small-scale S2S alignment with limited object diversity and without vision–language representations, leaving large-scale, open-set benchmarks for both F2S and S2S largely unexplored. In this work, we propose an automated annotation pipeline that constructs open-set 3D scene graphs from RGB-D images and poses by integrating foundation models with point cloud processing tools. Applying this pipeline to ScanNet, we build ScanNet-SG, a large-scale benchmark for 3D scene graph alignment. ScanNet-SG contains over 700k alignment samples and covers 509 object categories from ScanNet labels and over 3k categories from GPT-4o-based tagging. Each object node is enriched with semantic labels, BERT embeddings, vision–language features, object point clouds, and 3D bounding boxes. By providing large-scale, multimodal data and supporting both F2S and S2S settings, ScanNet-SG provides a comprehensive benchmark for training and evaluating robust 3D scene graph alignment in open-world environments.