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

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. Trainingfree 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 1 3D visual grounding, namely Language-to-Space Programming (LASP). LASP introduces LLMgenerated 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 trainingfree methods. Moreover, it substantially reduces the grounding time and token costs, offering a balanced trade-off between performance and efficiency. Code is available at https: //github.com/InternRobotics/LaSP .