title: MMPerspective: Do MLLMs Understand Perspective? A Comprehensive
    Benchmark for Perspective Perception, Reasoning, and Robustness
summary: Understanding perspective is fundamental to human visual perception, yet the
  extent to which multimodal large language models (MLLMs) internalize
  perspective geometry remains unclear. We introduce MMPerspective, the first
  benchmark specifically designed to systematically evaluate MLLMs' understanding
  of perspective through 10 carefully crafted tasks across three complementary
  dimensions: Perspective Perception, Reasoning, and Robustness. Our benchmark
  comprises 2,711 real-world and synthetic image instances with 5,083
  question-answer pairs that probe key capabilities, such as vanishing point
  perception and counting, perspective type reasoning, line relationship
  understanding in 3D space, invariance to perspective-preserving
  transformations, etc. Through a comprehensive evaluation of 43 state-of-the-art
  MLLMs, we uncover significant limitations: while models demonstrate competence
  on surface-level perceptual tasks, they struggle with compositional reasoning
  and maintaining spatial consistency under perturbations. Our analysis further
  reveals intriguing patterns between model architecture, scale, and perspective
  capabilities, highlighting both robustness bottlenecks and the benefits of
  chain-of-thought prompting. MMPerspective establishes a valuable testbed for
  diagnosing and advancing spatial understanding in vision-language systems.
  Resources available at: https://yunlong10.github.io/MMPerspective/
paper_authors._id: 683718530ab97eef8cefc1ce
paper_authors.name: Yunlong Tang
paper_authors._id: 683718530ab97eef8cefc1cf
paper_authors.name: Pinxin Liu
paper_authors._id: 683718530ab97eef8cefc1d0
paper_authors.name: Mingqian Feng
paper_authors._id: 683718530ab97eef8cefc1d1
paper_authors.name: Zhangyun Tan
paper_authors._id: 683718530ab97eef8cefc1d2
paper_authors.name: Rui Mao
paper_authors._id: 683718530ab97eef8cefc1d3
paper_authors.name: Chao Huang
paper_authors._id: 683718530ab97eef8cefc1d4
paper_authors.name: Jing Bi
paper_authors._id: 683718530ab97eef8cefc1d5
paper_authors.name: Yunzhong Xiao
paper_authors._id: 683718530ab97eef8cefc1d6
paper_authors.name: Susan Liang
paper_authors._id: 683718530ab97eef8cefc1d7
paper_authors.name: Hang Hua
paper_authors._id: 683718530ab97eef8cefc1d8
paper_authors.name: Ali Vosoughi
paper_authors._id: 683718530ab97eef8cefc1d9
paper_authors.name: Luchuan Song
paper_authors._id: 683718530ab97eef8cefc1da
paper_authors.name: Zeliang Zhang
paper_authors._id: 683718530ab97eef8cefc1db
paper_authors.name: Chenliang Xu
paper_title: MMPerspective: Do MLLMs Understand Perspective? A Comprehensive
    Benchmark for Perspective Perception, Reasoning, and Robustness
paper_summary: Understanding perspective is fundamental to human visual perception, yet the
  extent to which multimodal large language models (MLLMs) internalize
  perspective geometry remains unclear. We introduce MMPerspective, the first
  benchmark specifically designed to systematically evaluate MLLMs' understanding
  of perspective through 10 carefully crafted tasks across three complementary
  dimensions: Perspective Perception, Reasoning, and Robustness. Our benchmark
  comprises 2,711 real-world and synthetic image instances with 5,083
  question-answer pairs that probe key capabilities, such as vanishing point
  perception and counting, perspective type reasoning, line relationship
  understanding in 3D space, invariance to perspective-preserving
  transformations, etc. Through a comprehensive evaluation of 43 state-of-the-art
  MLLMs, we uncover significant limitations: while models demonstrate competence
  on surface-level perceptual tasks, they struggle with compositional reasoning
  and maintaining spatial consistency under perturbations. Our analysis further
  reveals intriguing patterns between model architecture, scale, and perspective
  capabilities, highlighting both robustness bottlenecks and the benefits of
  chain-of-thought prompting. MMPerspective establishes a valuable testbed for
  diagnosing and advancing spatial understanding in vision-language systems.
  Resources available at: https://yunlong10.github.io/MMPerspective/
paper_ai_summary: MMPerspective evaluates large language models' understanding of perspective geometry through tasks involving perception, reasoning, and robustness, revealing limitations in spatial reasoning and the benefits of chain-of-thought prompting.
paper_ai_keywords: multimodal large language models MMPerspective perspective geometry image instances question-answer pairs vanishing point perception perspective type reasoning line relationship understanding 3D space invariance compositional reasoning spatial consistency robustness chain-of-thought prompting vision-language systems