SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning
  Capabilities
Authors: name: Boyuan Chen, name: Zhuo Xu, name: Sean Kirmani, name: Brian Ichter, name: Danny Driess, name: Pete Florence, name: Dorsa Sadigh, name: Leonidas Guibas, name: Fei Xia

Understanding and reasoning about spatial relationships is a fundamental
capability for Visual Question Answering (VQA) and robotics. While Vision
Language Models (VLM) have demonstrated remarkable performance in certain VQA
benchmarks, they still lack capabilities in 3D spatial reasoning, such as
recognizing quantitative relationships of physical objects like distances or
size differences. We hypothesize that VLMs' limited spatial reasoning
capability is due to the lack of 3D spatial knowledge in training data and aim
to solve this problem by training VLMs with Internet-scale spatial reasoning
data. To this end, we present a system to facilitate this approach. We first
develop an automatic 3D spatial VQA data generation framework that scales up to
2 billion VQA examples on 10 million real-world images. We then investigate
various factors in the training recipe, including data quality, training
pipeline, and VLM architecture. Our work features the first internet-scale 3D
spatial reasoning dataset in metric space. By training a VLM on such data, we
significantly enhance its ability on both qualitative and quantitative spatial
VQA. Finally, we demonstrate that this VLM unlocks novel downstream
applications in chain-of-thought spatial reasoning and robotics due to its
quantitative estimation capability. Project website:
https://spatial-vlm.github.io/