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MMPerspective
MMPerspective
Do MLLMs Understand Perspective? A Comprehensive Benchmark for Perspective Perception, Reasoning, and Robustness
NeurIPS 2025
Yunlong Tang1*,
Pinxin Liu1*,
Zhangyun Tan1*,
Mingqian Feng1,
Rui Mao1,
Chao Huang1,
Jing Bi1,
Yunzhong Xiao2,
Susan Liang1,
Hang Hua1,
Ali Vosoughi1,
Luchuan Song1,
Zeliang Zhang1,
Chenliang Xu†1
* Equal Contribution, † Corresponding Author
* Equal Contribution, † Corresponding Author
1University of Rochester,
2Carnegie Mellon University
Introduction
Perspective has long served as a cornerstone for representing three-dimensional reality on two-dimensional surfaces, enabling humans to infer spatial structure and depth from flat images. This capability is central to artistic creation, scientific visualization, and machine perception. While some research has attempted to enable models to detect vanishing points and key lines, these approaches often rely on precise mathematical models or specialized datasets, struggling to capture perspective-related semantics or generalize to broader tasks.
Recent multimodal large language models like GPT-4 and Gemini have shown powerful visual perception capabilities, but their perspective understanding remains untested. While these models excel at high-level vision-language tasks, existing benchmarks rarely evaluate geometric reasoning abilities. It remains unclear whether MLLMs can identify vanishing points, understand line convergence, reason about spatial relationships, or maintain consistent interpretations across viewpoints.
Our key contributions are:
- We introduce MMPerspective, the first dedicated benchmark for evaluating perspective understanding in MLLMs, spanning 10 tasks across three dimensions, consisting of 2,711 instances and 5,083 QA pairs.
- We conduct a comprehensive evaluation of 43 representative MLLMs and reveal key limitations in perspective perception, reasoning, and robustness.
- We offer new insights into current model bottlenecks and provide guidance toward building geometry-aware, spatially grounded multimodal systems.
🏆 Leaderboard
Performance of MLLMs on MMPerspective. Models are grouped by size and ranked by overall accuracy. Best scores in each group are bolded.
| Model | Perspective Perception | Perspective Reasoning | P'Percep & P'Reason | Robustness | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model | VPP | CLP | VAP | LDP | PTR | LRR | OVR | PTS | VPC | P Acc | R Acc | Overall | P'Robust |
| MLLMs: < 7B | |||||||||||||
| InternVL2.5-2B | 47.4 | 22.8 | 13.0 | 65.3 | 62.2 | 31.8 | 16.6 | 30.0 | 50.0 | 37.1 | 38.1 | 37.7 | 46.5 |
| Qwen2.5-VL-3B | 27.6 | 22.8 | 56.8 | 55.1 | 32.3 | 32.5 | 15.9 | 39.4 | 44.7 | 40.6 | 33.0 | 36.3 | 6.4 |
| InternVL2.5-4B | 32.1 | 26.0 | 59.3 | 64.2 | 28.2 | 30.5 | 10.7 | 37.1 | 36.8 | 45.4 | 28.7 | 36.1 | 20.6 |
| InternVL3-2B | 22.4 | 28.5 | 50.0 | 44.6 | 43.1 | 31.1 | 34.4 | 25.4 | 43.0 | 36.4 | 35.4 | 35.8 | 23.9 |
| InternVL2-4B | 26.9 | 12.2 | 54.3 | 60.4 | 18.0 | 40.4 | 18.8 | 24.4 | 45.6 | 38.4 | 29.4 | 33.4 | 7.9 |
| Qwen2-VL-2B | 12.2 | 19.5 | 49.4 | 35.8 | 23.3 | 24.5 | 28.9 | 32.9 | 47.4 | 29.2 | 31.4 | 30.4 | 4.7 |
| InternVL3-1B | 19.9 | 13.0 | 53.7 | 20.7 | 16.3 | 8.6 | 23.7 | 21.6 | 47.4 | 26.8 | 23.5 | 25.0 | 13.8 |
| InternVL2-1B | 20.5 | 20.3 | 15.4 | 24.2 | 24.1 | 11.3 | 24.0 | 22.1 | 44.7 | 20.1 | 25.2 | 23.0 | 6.7 |
| LLaVA-OV-1B | 13.5 | 14.6 | 35.8 | 24.2 | 15.2 | 19.2 | 19.5 | 22.1 | 40.4 | 22.0 | 23.3 | 22.7 | 7.8 |
| InternVL2-2B | 26.9 | 26.0 | 3.1 | 36.8 | 18.8 | 12.6 | 23.1 | 21.1 | 34.2 | 23.2 | 22.0 | 22.5 | 12.3 |
| InternVL2.5-1B | 14.7 | 23.6 | 0.6 | 33.0 | 20.1 | 11.3 | 13.3 | 34.7 | 45.6 | 18.0 | 25.0 | 21.9 | 18.2 |
| MLLMs: 7B - 9B | |||||||||||||
| InternVL2.5-8B | 38.5 | 17.9 | 53.1 | 75.4 | 40.8 | 48.3 | 34.7 | 24.9 | 67.5 | 46.2 | 43.3 | 44.6 | 22.3 |
| Qwen2.5-VL-7B | 35.3 | 29.3 | 70.4 | 73.7 | 42.4 | 44.4 | 32.1 | 28.6 | 44.7 | 52.1 | 38.5 | 44.5 | 15.3 |
| Qwen2-VL-7B | 34.6 | 25.2 | 63.0 | 64.2 | 57.1 | 49.0 | 27.3 | 31.0 | 46.5 | 46.7 | 42.2 | 44.2 | 25.5 |
| InternVL3-9B | 37.2 | 33.3 | 63.0 | 77.5 | 30.7 | 53.0 | 27.9 | 23.9 | 43.9 | 52.8 | 35.9 | 43.4 | 7.3 |
| InternVL3-8B | 42.3 | 27.6 | 67.9 | 81.8 | 38.1 | 46.4 | 20.8 | 23.9 | 32.5 | 54.9 | 32.3 | 42.4 | 15.9 |
| LLaVA-OV-7B | 34.0 | 33.3 | 51.2 | 57.9 | 44.9 | 53.0 | 19.8 | 35.2 | 49.1 | 44.1 | 40.4 | 42.0 | 15.9 |
| Eagle-X4-8B | 39.1 | 17.1 | 46.9 | 47.7 | 65.3 | 37.1 | 18.2 | 32.9 | 68.4 | 37.7 | 44.4 | 41.4 | 55.3 |
| InternVL2-8B | 33.3 | 19.5 | 59.3 | 73.3 | 27.1 | 36.4 | 42.5 | 22.1 | 48.2 | 46.4 | 35.3 | 40.2 | 7.9 |
| LLaVA-Next-m-7B | 35.9 | 21.1 | 35.2 | 50.5 | 17.7 | 37.7 | 15.6 | 27.2 | 46.5 | 35.7 | 28.9 | 31.9 | 16.4 |
| Eagle-X5-7B | 25.0 | 26.0 | 24.7 | 34.7 | 22.1 | 46.4 | 15.6 | 20.7 | 42.1 | 27.6 | 29.4 | 28.6 | 15.9 |
| LLaVA-Next-v-7B | 16.7 | 20.3 | 40.7 | 39.6 | 16.3 | 44.4 | 19.8 | 16.4 | 7.0 | 29.3 | 20.8 | 24.6 | 16.4 |
| MLLMs: 10B - 30B | |||||||||||||
| InternVL2.5-26B | 41.7 | 35.0 | 55.6 | 81.8 | 65.5 | 46.4 | 43.5 | 34.3 | 46.5 | 53.5 | 47.2 | 50.0 | 33.7 |
| InternVL3-14B | 39.1 | 26.0 | 73.5 | 73.3 | 36.5 | 34.4 | 54.5 | 28.2 | 54.4 | 53.0 | 41.6 | 46.7 | 13.5 |
| InternVL2-26B | 28.2 | 35.0 | 61.1 | 74.0 | 50.7 | 41.7 | 28.9 | 28.6 | 43.0 | 49.6 | 38.6 | 43.5 | 26.5 |
| Eagle-X4-13B | 42.3 | 26.8 | 41.4 | 44.6 | 65.8 | 20.5 | 28.2 | 31.0 | 57.9 | 38.8 | 40.7 | 39.8 | 53.8 |
| LLaVA-Next-13B | 7.7 | 17.1 | 54.3 | 34.7 | 66.7 | 24.5 | 13.0 | 26.8 | 43.9 | 28.5 | 35.0 | 32.1 | 51.1 |
| MLLMs: 30B - 70B | |||||||||||||
| InternVL2.5-38B | 46.8 | 36.6 | 67.9 | 89.5 | 58.4 | 51.7 | 38.3 | 44.1 | 44.7 | 60.2 | 47.5 | 53.1 | 19.1 |
| InternVL3-38B | 45.5 | 35.0 | 71.0 | 90.9 | 37.3 | 43.0 | 56.8 | 37.6 | 43.0 | 60.6 | 43.5 | 51.1 | 9.1 |
| Qwen2.5-VL-32B | 35.9 | 22.8 | 68.5 | 73.7 | 62.0 | 37.7 | 33.8 | 35.2 | 45.6 | 50.2 | 42.9 | 46.1 | 25.5 |
| Eagle-X5-34B | 36.5 | 28.5 | 60.5 | 79.6 | 19.5 | 51.0 | 24.0 | 39.0 | 63.2 | 51.3 | 39.3 | 44.6 | 16.0 |
| InternVL2-40B | 26.3 | 22.0 | 66.0 | 76.1 | 43.2 | 55.0 | 27.3 | 25.8 | 47.4 | 47.6 | 39.7 | 43.2 | 12.6 |
| MLLMs: > 70B | |||||||||||||
| InternVL3-78B | 43.6 | 39.8 | 69.8 | 89.1 | 55.9 | 57.6 | 40.3 | 38.0 | 42.1 | 60.6 | 46.8 | 52.9 | 25.5 |
| InternVL2.5-72B | 47.4 | 30.1 | 67.3 | 89.5 | 65.2 | 53.6 | 41.9 | 32.4 | 37.7 | 58.6 | 46.2 | 51.7 | 29.7 |
| Qwen2.5-VL-72B | 41.7 | 31.7 | 67.9 | 82.1 | 65.3 | 38.4 | 39.9 | 39.0 | 38.6 | 55.8 | 44.3 | 49.4 | 24.3 |
| Qwen2-VL-72B | 34.6 | 18.7 | 70.4 | 82.5 | 68.8 | 52.3 | 38.6 | 35.2 | 42.1 | 51.5 | 47.4 | 49.2 | 25.0 |
| LLaVA-OV-72B | 25.6 | 26.0 | 75.9 | 81.1 | 81.4 | 55.6 | 22.4 | 28.2 | 31.6 | 52.2 | 43.8 | 47.5 | 53.1 |
| LLaVA-Next-72B | 21.8 | 21.1 | 66.0 | 32.3 | 65.7 | 49.7 | 22.4 | 27.2 | 30.7 | 35.3 | 39.1 | 37.4 | 33.2 |
| InternVL2-72B | 26.9 | 18.7 | 57.4 | 56.8 | 56.1 | 47.0 | 24.7 | 24.4 | 7.9 | 40.0 | 32.0 | 35.6 | 22.9 |
| MLLMs: Proprietary | |||||||||||||
| Gemini-2-flash (CoT) | 69.2 | 49.6 | 72.8 | 87.4 | 78.7 | 32.5 | 40.9 | 39.9 | 43.9 | 69.8 | 47.2 | 57.2 | 45.9 |
| GPT-4o (CoT) | 45.5 | 46.3 | 70.4 | 88.8 | 81.4 | 47.0 | 34.4 | 37.6 | 34.2 | 62.7 | 46.9 | 54.0 | 49.9 |
| Gemini-2-flash | 64.7 | 35.0 | 73.5 | 87.0 | 71.3 | 34.4 | 29.9 | 40.8 | 41.2 | 65.0 | 43.5 | 53.1 | 30.7 |
| GPT-4o | 42.9 | 35.0 | 66.0 | 86.0 | 82.0 | 41.7 | 29.9 | 33.8 | 32.5 | 57.5 | 44.0 | 50.0 | 49.9 |
| Gemini-1.5-flash (CoT) | 30.1 | 28.5 | 66.7 | 79.3 | 51.0 | 39.7 | 20.1 | 31.5 | 35.1 | 51.1 | 35.5 | 42.4 | 15.3 |
| GPT-4o-mini | 35.3 | 24.4 | 43.2 | 71.6 | 43.1 | 29.8 | 14.6 | 31.0 | 45.6 | 43.6 | 32.8 | 37.6 | 10.8 |
| Gemini-1.5-flash | 26.9 | 25.2 | 59.3 | 70.5 | 26.4 | 27.8 | 18.2 | 26.8 | 22.8 | 45.5 | 24.4 | 33.8 | 10.6 |
Benchmark
Data Examples
Samples for Robustness Evaluation
Benchmark Statistics
(Left) Task Categorie Hierarchy: MMPerspective consists of 3 key task types and 10 subcategories.
(Right) SOTA Comparison: Performance of SOTA MLLMs on MMPerspective.
Data Curation Pipeline
The data curation pipeline for MMPerspective.
Experiment Results
Results Analysis
SOTA Models Performance on MMPerspective
Heatmaps illustrating the relationship between model size and performance, measured by P\&R Overall Accuracy and Robustness. Darker colors indicate higher performance. Each line represents a model family, with sizes increasing from left to right.
Correlation analysis between performance and size across MLLM families: (a) Overall accuracy vs. model size ($r = 0.81$), (b) Robustness vs. model size ($r = 0.34$), (c) Overall accuracy vs. encoder size ($r = 0.51$), (d) Robustness vs. encoder size ($r = 0.15$). Total model scaling strongly impacts perspective understanding, while vision encoder size has a limited influence on robustness.
Error pattern analysis across model families: Cumulative distribution of phi coefficients shows significantly higher correlations within families than across families (Cohen's d=0.33, p < 0.001).
Task-wise breakdown reveals perception tasks (VAP, CLP) exhibit the strongest family-specific patterns, while reasoning tasks (VPC, LRR) show weaker family effects.
Difficulty Distribution of MMPerspective
Chain-of-Thought Examples
Citation
@article{tang2025mmperspective,
title={MMPerspective: Do MLLMs Understand Perspective? A Comprehensive Benchmark for Perspective Perception, Reasoning, and Robustness},
author={Tang, Yunlong and Liu, Pinxin and Tan, Zhangyun and Feng, Mingqian and Mao, Rui and Huang, Chao and Bi, Jing and Xiao, Yunzhong and Liang, Susan and Hua, Hang and others},
journal={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2025}
}