MMPerspective: Do MLLMs Understand Perspective? A Comprehensive
  Benchmark for Perspective Perception, Reasoning, and Robustness
Authors: name: Yunlong Tang, name: Pinxin Liu, name: Mingqian Feng, name: Zhangyun Tan, name: Rui Mao, name: Chao Huang, name: Jing Bi, name: Yunzhong Xiao, name: Susan Liang, name: Hang Hua, name: Ali Vosoughi, name: Luchuan Song, name: Zeliang Zhang, name: Chenliang Xu

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/