Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial
  Intelligence
Authors: name: Diankun Wu, name: Fangfu Liu, name: Yi-Hsin Hung, name: Yueqi Duan

Recent advancements in Multimodal Large Language Models (MLLMs) have
significantly enhanced performance on 2D visual tasks. However, improving their
spatial intelligence remains a challenge. Existing 3D MLLMs always rely on
additional 3D or 2.5D data to incorporate spatial awareness, restricting their
utility in scenarios with only 2D inputs, such as images or videos. In this
paper, we present Spatial-MLLM, a novel framework for visual-based spatial
reasoning from purely 2D observations. Unlike conventional video MLLMs which
rely on CLIP-based visual encoders optimized for semantic understanding, our
key insight is to unleash the strong structure prior from the feed-forward
visual geometry foundation model. Specifically, we propose a dual-encoder
architecture: a pretrained 2D visual encoder to extract semantic features, and
a spatial encoder-initialized from the backbone of the visual geometry model-to
extract 3D structure features. A connector then integrates both features into
unified visual tokens for enhanced spatial understanding. Furthermore, we
propose a space-aware frame sampling strategy at inference time, which selects
the spatially informative frames of a video sequence, ensuring that even under
limited token length, the model focuses on frames critical for spatial
reasoning. Beyond architecture improvements, we construct the Spatial-MLLM-120k
dataset and train the model on it using supervised fine-tuning and GRPO.
Extensive experiments on various real-world datasets demonstrate that our
spatial-MLLM achieves state-of-the-art performance in a wide range of
visual-based spatial understanding and reasoning tasks. Project page:
https://diankun-wu.github.io/Spatial-MLLM/.