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New ReRef-3D benchmark tests AI's ability to place objects in 3D scenes

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced ReRef-3D, a new benchmark designed to evaluate language-guided object placement in 3D environments. The benchmark comprises over 33,000 instructions across nearly 1,000 scenes, focusing on various referencing complexities. Initial evaluations show that LLaVA-3D achieved the highest performance, correctly placing objects in 68.3% of cases, significantly outperforming 3D-LLM and PlaceIt3D. The study also found that relational difficulties, such as 'nearest' or 'between,' pose the greatest challenge for current models. AI

IMPACT Establishes a new standard for evaluating spatial reasoning and object manipulation in AI models within 3D environments.

RANK_REASON Publication of a new benchmark and research paper on arXiv.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New ReRef-3D benchmark tests AI's ability to place objects in 3D scenes

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Mary Lynn Martin, Yifei Zhang, Martha Palmer, Maria Leonor Pacheco ·

    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 instruc…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    ReRef-3D: A Benchmark for Spatial Referring Expression-Guided 3D Scene Rearrangement

    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…

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