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arXiv AI By Minsu Kim, Jaesung Choe, Jiwoo Lee, Yu-Chiang Frank Wang, Seon Joo Kim

RelationVGGT: Visual Geometry Transformers for 3D Spatial Relation Segmentation

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The paper introduces RelationVGGT, a feed‑forward framework that performs 3D spatial relation segmentation without per‑scene optimization or known camera poses. It combines semantic features from a visual foundation model with geometry‑aware representations from a 3D geometry foundation model, and uses a relation transformer to predict subject‑conditioned, cross‑view relations based on a visual subject and a textual query. The authors also present an automated annotation pipeline built on ScanNet++ with VLMs and LLMs to generate scalable training data for this new task.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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