Language-to-Space Programming for Training-Free 3D Visual Grounding

ACL ARR 2025 May Submission2712 Authors

Abstract:

3D visual grounding (3DVG) is challenging due to the need to understand 3D spatial relations.
While supervised approaches have achieved superior performance, they are constrained by the scarcity and high annotation costs of 3D vision-language datasets.
Training-free approaches based on LLMs/VLMs eliminate the need for large-scale training data, but they either incur prohibitive grounding time and token costs or have unsatisfactory accuracy.
To address the challenges, we introduce a novel method for training-free 3D visual grounding, namely
**La**nguage-to-**S**pace **P**rogramming (LaSP).
LaSP introduces LLM-generated codes to analyze 3D spatial relations among objects, along with a pipeline that evaluates and optimizes the codes automatically.
Experimental results demonstrate that LaSP achieves 52.9\% accuracy on the Nr3D benchmark, ranking among the best training-free methods.
Moreover, it substantially reduces the grounding time and token costs, offering a balanced trade-off between performance and efficiency.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: Multimodality and Language Grounding to Vision, Robotics and Beyond

Contribution Types: NLP engineering experiment

Languages Studied: English

Keywords: Multimodality and Language Grounding to Vision, Robotics and Beyond

Submission Number: 2712