SD-VLM: Spatial Measuring and Understanding with Depth-Encoded Vision-Language Models

• URL: http://arxiv.org/abs/2509.17664v1
• Date: Mon, 22 Sep 2025 12:08:12 GMT
• Title: SD-VLM: Spatial Measuring and Understanding with Depth-Encoded Vision-Language Models
• Authors: Pingyi Chen, Yujing Lou, Shen Cao, Jinhui Guo, Lubin Fan, Yue Wu, Lin Yang, Lizhuang Ma, Jieping Ye,
• Abstract summary: Vision language models (VLMs) excel in 2D semantic visual understanding, but their ability to quantitatively reason about 3D spatial relationships remains under-explored.<n>We propose SD-VLM, a novel framework that significantly enhances fundamental spatial perception abilities of VLMs.<n>We have trained SD-VLM, a strong generalist VLM which shows superior quantitative spatial measuring and understanding capability.
• Score: 75.64836077468722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While vision language models (VLMs) excel in 2D semantic visual understanding, their ability to quantitatively reason about 3D spatial relationships remains under-explored, due to the deficiency of 2D images' spatial representation ability. In this paper, we analyze the problem hindering VLMs' spatial understanding abilities and propose SD-VLM, a novel framework that significantly enhances fundamental spatial perception abilities of VLMs through two key contributions: (1) propose Massive Spatial Measuring and Understanding (MSMU) dataset with precise spatial annotations, and (2) introduce a simple depth positional encoding method strengthening VLMs' spatial awareness. MSMU dataset covers massive quantitative spatial tasks with 700K QA pairs, 2.5M physical numerical annotations, and 10K chain-of-thought augmented samples. We have trained SD-VLM, a strong generalist VLM which shows superior quantitative spatial measuring and understanding capability. SD-VLM not only achieves state-of-the-art performance on our proposed MSMU-Bench, but also shows spatial generalization abilities on other spatial understanding benchmarks including Q-Spatial and SpatialRGPT-Bench. Extensive experiments demonstrate that SD-VLM outperforms GPT-4o and Intern-VL3-78B by 26.91% and 25.56% respectively on MSMU-Bench. Code and models are released at https://github.com/cpystan/SD-VLM.

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