From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes

Tianxu Wang1, Zhuofan Zhang1,2, Ziyu Zhu1,2, Yue Fan1,
Jing Xiong1,3, Pengxiang Li1,4, Xiaojian Ma1, Qing Li1📧
1BIGAI 2Tsinghua University 3Peking University 4Beijing Institute of Technology
arXiv Video Code Data Annotator

TL;DR We introduce Anywhere3D-Bench, a holistic 3D visual grounding benchmark consisting of 2.6K referring expression-3D bounding box pairs spanning four different grounding levels: human-activity areas, unoccupied space beyond objects, objects in the scene, and fine-grained object parts.




Abstract

3D visual grounding has made notable progress in localizing objects within complex 3D scenes. However, grounding referring expressions beyond objects in 3D scenes remains unexplored. In this paper, we introduce Anywhere3D-Bench, a holistic 3D visual grounding benchmark consisting of 2,632 referring expression-3D bounding box pairs spanning four different grounding levels: human-activity areas, unoccupied space beyond objects, objects in the scene, and fine-grained object parts. We assess a range of state-of-the-art 3D visual grounding methods alongside large language models (LLMs) and multimodal LLMs (MLLMs) on Anywhere3D-Bench. Experimental results reveal that space-level and part-level visual grounding pose the greatest challenges: space-level tasks require a more comprehensive spatial reasoning ability, for example, modeling distances and spatial relations within 3D space, while part-level tasks demand fine-grained perception of object composition. Even the best performance model, OpenAI o4-mini, achieves only 23.57% accuracy on space-level tasks and 33.94% on part-level tasks, significantly lower than its performance on area-level and object-level tasks. These findings underscore a critical gap in current models' capacity to understand and reason about 3D scene beyond object-level semantics.


Dataset

Anywhere3D-Bench involves multi-level 3D visual grounding (part, object, area, space) with distinct expression types.

Data Exploration

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Quantitative Results

Results presented in Acc@0.25IoU on Anywhere3D-Bench.

Qualitative Results

Here we present a few qualitative results from Anywhere3D-Bench with GPT-4.1's reasoning processes.

BibTeX

@misc{anywhere3d,
      title={From Objects to Anywhere: A Holistic Benchmark for Multi-level Visual Grounding in 3D Scenes}, 
      author={Tianxu Wang and Zhuofan Zhang and Ziyu Zhu and Yue Fan and Jing Xiong and Pengxiang Li and Xiaojian Ma and Qing Li},
      year={2025},
      eprint={2506.04897},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.04897}, 
}