  Current state-of-the-art spatial reasoning-enhanced VLMs are trained to excel
at spatial visual question answering (VQA). However, we believe that
higher-level 3D-aware tasks, such as articulating dynamic scene changes and
motion planning, require a fundamental and explicit 3D understanding beyond
current spatial VQA datasets. In this work, we present SpatialPIN, a framework
designed to enhance the spatial reasoning capabilities of VLMs through
prompting and interacting with priors from multiple 3D foundation models in a
zero-shot, training-free manner. Extensive experiments demonstrate that our
spatial reasoning-imbued VLM performs well on various forms of spatial VQA and
can extend to help in various downstream robotics tasks such as pick and stack
and trajectory planning.
