MMPerspective: Do MLLMs Understand Perspective? A Comprehensive
  Benchmark for Perspective Perception, Reasoning, and Robustness
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/
683718530ab97eef8cefc1ce
Yunlong Tang
683718530ab97eef8cefc1cf
Pinxin Liu
683718530ab97eef8cefc1d0
Mingqian Feng
683718530ab97eef8cefc1d1
Zhangyun Tan
683718530ab97eef8cefc1d2
Rui Mao
683718530ab97eef8cefc1d3
Chao Huang
683718530ab97eef8cefc1d4
Jing Bi
683718530ab97eef8cefc1d5
Yunzhong Xiao
683718530ab97eef8cefc1d6
Susan Liang
683718530ab97eef8cefc1d7
Hang Hua
683718530ab97eef8cefc1d8
Ali Vosoughi
683718530ab97eef8cefc1d9
Luchuan Song
683718530ab97eef8cefc1da
Zeliang Zhang
683718530ab97eef8cefc1db
Chenliang Xu
MMPerspective: Do MLLMs Understand Perspective? A Comprehensive
  Benchmark for Perspective Perception, Reasoning, and Robustness
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/
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.
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