  In this work, we present a new visual prompting method called 3DAxiesPrompts
(3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks.
Our investigation reveals that while GPT-4V exhibits proficiency in discerning
the position and interrelations of 2D entities through current visual prompting
techniques, its abilities in handling 3D spatial tasks have yet to be explored.
In our approach, we create a 3D coordinate system tailored to 3D imagery,
complete with annotated scale information. By presenting images infused with
the 3DAP visual prompt as inputs, we empower GPT-4V to ascertain the spatial
positioning information of the given 3D target image with a high degree of
precision. Through experiments, We identified three tasks that could be stably
completed using the 3DAP method, namely, 2D to 3D Point Reconstruction, 2D to
3D point matching, and 3D Object Detection. We perform experiments on our
proposed dataset 3DAP-Data, the results from these experiments validate the
efficacy of 3DAP-enhanced GPT-4V inputs, marking a significant stride in 3D
spatial task execution.
