title: SSR: Enhancing Depth Perception in Vision-Language Models via
    Rationale-Guided Spatial Reasoning
summary: Despite impressive advancements in Visual-Language Models (VLMs) for
  multi-modal tasks, their reliance on RGB inputs limits precise spatial
  understanding. Existing methods for integrating spatial cues, such as point
  clouds or depth, either require specialized sensors or fail to effectively
  exploit depth information for higher-order reasoning. To this end, we propose a
  novel Spatial Sense and Reasoning method, dubbed SSR, a novel framework that
  transforms raw depth data into structured, interpretable textual rationales.
  These textual rationales serve as meaningful intermediate representations to
  significantly enhance spatial reasoning capabilities. Additionally, we leverage
  knowledge distillation to compress the generated rationales into compact latent
  embeddings, which facilitate resource-efficient and plug-and-play integration
  into existing VLMs without retraining. To enable comprehensive evaluation, we
  introduce a new dataset named SSR-CoT, a million-scale visual-language
  reasoning dataset enriched with intermediate spatial reasoning annotations, and
  present SSRBench, a comprehensive multi-task benchmark. Extensive experiments
  on multiple benchmarks demonstrate SSR substantially improves depth utilization
  and enhances spatial reasoning, thereby advancing VLMs toward more human-like
  multi-modal understanding. Our project page is at
  https://yliu-cs.github.io/SSR.
paper_authors._id: 682d47aa64daf8623f1f5604
paper_authors.name: Yang Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:40:37.342Z
paper_authors._id: 6586817e509bcae23f3dfc60
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pwEStoey1XJYDi3F0Z930.png
paper_authors.fullname: Yang Liu
paper_authors.type: user
paper_authors.user: yliu-cs
paper_authors._id: 682d47aa64daf8623f1f5605
paper_authors.name: Ming Ma
paper_authors._id: 682d47aa64daf8623f1f5606
paper_authors.name: Xiaomin Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T15:17:46.958Z
paper_authors._id: 64084fa192033c150738e4f2
paper_authors.avatarUrl: /avatars/dfff2216eb235c635e5abe6fda3084f0.svg
paper_authors.fullname: Yu_xm
paper_authors.type: user
paper_authors.user: Yu2020
paper_authors._id: 682d47aa64daf8623f1f5607
paper_authors.name: Pengxiang Ding
paper_authors._id: 682d47aa64daf8623f1f5608
paper_authors.name: Han Zhao
paper_authors._id: 682d47aa64daf8623f1f5609
paper_authors.name: Mingyang Sun
paper_authors._id: 682d47aa64daf8623f1f560a
paper_authors.name: Siteng Huang
paper_authors._id: 682d47aa64daf8623f1f560b
paper_authors.name: Donglin Wang
paper_title: SSR: Enhancing Depth Perception in Vision-Language Models via
    Rationale-Guided Spatial Reasoning
paper_summary: Despite impressive advancements in Visual-Language Models (VLMs) for
  multi-modal tasks, their reliance on RGB inputs limits precise spatial
  understanding. Existing methods for integrating spatial cues, such as point
  clouds or depth, either require specialized sensors or fail to effectively
  exploit depth information for higher-order reasoning. To this end, we propose a
  novel Spatial Sense and Reasoning method, dubbed SSR, a novel framework that
  transforms raw depth data into structured, interpretable textual rationales.
  These textual rationales serve as meaningful intermediate representations to
  significantly enhance spatial reasoning capabilities. Additionally, we leverage
  knowledge distillation to compress the generated rationales into compact latent
  embeddings, which facilitate resource-efficient and plug-and-play integration
  into existing VLMs without retraining. To enable comprehensive evaluation, we
  introduce a new dataset named SSR-CoT, a million-scale visual-language
  reasoning dataset enriched with intermediate spatial reasoning annotations, and
  present SSRBench, a comprehensive multi-task benchmark. Extensive experiments
  on multiple benchmarks demonstrate SSR substantially improves depth utilization
  and enhances spatial reasoning, thereby advancing VLMs toward more human-like
  multi-modal understanding. Our project page is at
  https://yliu-cs.github.io/SSR.
paper_ai_summary: A novel method transforms depth data into textual rationales to enhance spatial reasoning in Visual-Language Models, improving depth utilization and human-like multi-modal understanding.
paper_ai_keywords: Spatial Sense and Reasoning SSR structured interpretable textual rationales knowledge distillation latent embeddings SSR-CoT SSRBench multi-task benchmark visual-language reasoning