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ReRef-3D establishes a rigorous new benchmark for spatial reasoning in 3D environments, revealing significant gaps between current models' ability to satisfy relational constraints versus maintaining physical validity. The study highlights that complex relative positioning tasks remain the primary bottleneck for language-guided scene rearrangement systems using LLaVA-3D and similar architectures. This work advances machine learning by providing a standardized testbed specifically designed to evaluate multi-hop spatial understanding beyond simple object placement.
ReRef-3D: A Benchmark for Spatial Referring Expression-Guided 3D Scene Rearrangement
arXiv:2608.16011v1 Announce Type: new Abstract: We introduce ReRef-3D, a benchmark for language-guided placement in 3D scenes. It contains 33,826 instructions across 998 CLEVR-derived scenes, spanning 16 placement families and direct, one-hop, and two-hop references. Each instruction must be resolved into a valid new placement position. Given that an instruction defines a region of acceptable placements rather than one coordinate, our evaluation inserts a prediction into the scene, recomputes relations, and tests relation satisfaction and physical validity. Each instruction also includes a verified naturalized rewrite. After fine-tuning, LLaVA-3D, 3D-LLM, and PlaceIt3D produce valid placements for 68.3%, 31.6%, and 22.4% of instructions, respectively. Across models, relation satisfaction surpasses physical validity, relations such as nearest and between are the most difficult, and phrasing has minimal effect on performance.
Abstract:We introduce ReRef-3D, a benchmark for language-guided placement in 3D scenes. It contains 33,826 instructions across 998 CLEVR-derived scenes, spanning 16 placement families and direct, one-hop, and two-hop references. Each instruction must be resolved into a valid new placement position. Given that an instruction defines a region of acceptable placements rather than one coordinate, our evaluation inserts a prediction into the scene, recomputes relations, and tests relation satisfaction and physical validity. Each instruction also includes a verified naturalized rewrite. After fine-tuning, LLaVA-3D, 3D-LLM, and PlaceIt3D produce valid placements for 68.3%, 31.6%, and 22.4% of instructions, respectively. Across models, relation satisfaction surpasses physical validity, relations such as nearest and between are the most difficult, and phrasing has minimal effect on performance.
18 pages, 4 figures. Submitted to ACL Rolling Review (ARR)
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2608.16011 [cs.CL]
(or arXiv:2608.16011v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.16011 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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