{"data":{"id":"10.4121/bebe8bd4-cf91-4f86-a28a-87cb870f6cea","type":"dois","attributes":{"doi":"10.4121/bebe8bd4-cf91-4f86-a28a-87cb870f6cea","prefix":"10.4121","suffix":"bebe8bd4-cf91-4f86-a28a-87cb870f6cea","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Chen, Gang","nameType":"Personal","givenName":"Gang","familyName":"Chen","nameIdentifiers":[{"nameIdentifier":"0000-0002-0124-2752","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Barbas Laina, Sebastian","nameType":"Personal","givenName":"Sebastian","familyName":"Barbas Laina","nameIdentifiers":[{"nameIdentifier":"0009-0007-7357-0329","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Alonso-Mora, Javier","nameType":"Personal","givenName":"Javier","familyName":"Alonso-Mora","nameIdentifiers":[{"nameIdentifier":"0000-0003-0058-570X","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"ScanNet-SG: A Large-Scale Dataset for 3D Scene Graph Alignment"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"Scene Graph"},{"subject":"mapping"},{"subject":"Computer vision"},{"subject":"Robotics"}],"contributors":[{"name":"TU Delft, Faculty of Mechanical Engineering, Department of Cognitive robotics, Learning and Autonomous Control","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2026-04-16","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/bebe8bd4-cf91-4f86-a28a-87cb870f6cea.v1","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/bebe8bd4-cf91-4f86-a28a-87cb870f6cea.v2","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/bebe8bd4-cf91-4f86-a28a-87cb870f6cea.v3","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["pointcloud/.ply, image/.png, scene graph/.json, meta info/.csv or .txt"],"version":"3","rightsList":[{"rights":"Creative Commons Attribution Non Commercial 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-nc-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"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.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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