  Visual Language Models (VLMs) are essential for various tasks, particularly
visual reasoning tasks, due to their robust multi-modal information
integration, visual reasoning capabilities, and contextual awareness. However,
existing \VLMs{}' visual spatial reasoning capabilities are often inadequate,
struggling even with basic tasks such as distinguishing left from right. To
address this, we propose the \ours{} model, designed to enhance the visual
spatial reasoning abilities of VLMS. ZeroVLM employs Zero-1-to-3, a 3D
reconstruction model for obtaining different views of the input images and
incorporates a prompting mechanism to further improve visual spatial reasoning.
Experimental results on four visual spatial reasoning datasets show that our
\ours{} achieves up to 19.48% accuracy improvement, which indicates the
effectiveness of the 3D reconstruction and prompting mechanisms of our ZeroVLM.
