  Vision Language Models (VLMs) have achieved impressive performance in 2D
image understanding, however they are still struggling with spatial
understanding which is the foundation of Embodied AI. In this paper, we propose
SpatialBot for better spatial understanding by feeding both RGB and depth
images. Additionally, we have constructed the SpatialQA dataset, which involves
multi-level depth-related questions to train VLMs for depth understanding.
Finally, we present SpatialBench to comprehensively evaluate VLMs' capabilities
in spatial understanding at different levels. Extensive experiments on our
spatial-understanding benchmark, general VLM benchmarks and Embodied AI tasks,
demonstrate the remarkable improvements of SpatialBot trained on SpatialQA. The
model, code and data are available at https://github.com/BAAI-DCAI/SpatialBot.
