SpaceDG: Benchmarking Spatial Intelligence under Visual Degradation

Hugging Face Daily Papers Papers
spatial-intelligence multimodal-language-models visual-degradation robustness benchmark

Summary

SpaceDG is a large-scale dataset and benchmark that evaluates multimodal language models' spatial reasoning robustness under visual degradations like motion blur and low light, revealing significant performance gaps and showing that fine-tuning on SpaceDG improves robustness without degrading clean image performance.

Multimodal Large Language Models (MLLMs) have made rapid progress in spatial intelligence, yet existing spatial reasoning benchmarks largely assume pristine visual inputs and overlook the degradations that commonly occur in real-world deployment, such as motion blur, low light, adverse weather, lens distortion, and compression artifacts. This raises a fundamental question: how robust is the spatial intelligence of current MLLMs when visual observations are imperfect? To answer this question, we introduce SpaceDG, the first large-scale dataset for degradation-aware spatial understanding. It is constructed with a physically grounded degradation synthesis engine that embeds degradation formation process into 3D Gaussian Splatting (3DGS) rendering, enabling realistic simulation of nine degradation types. The resulting dataset contains approximately 1M QA pairs from nearly 1,000 indoor scenes. We further introduce SpaceDG-Bench, an human-verified benchmark with 1,102 questions spanning 11 reasoning categories and 9 visual degradation types, yielding over 10K VQA instances. Evaluating 25 open- and closed-source MLLMs reveals that visual degradations consistently and substantially impair spatial reasoning, exposing a critical robustness gap. Finally, we show that finetuning on SpaceDG markedly improves degradation robustness and can even surpass human performance under degraded conditions without any performance drop on clean images, highlighting the promise of degradation-aware training for robust spatial intelligence.
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Abstract

SpaceDG dataset and benchmark evaluate multimodal language models’ spatial reasoning robustness under visual degradations, revealing significant performance gaps and demonstrating improved robustness through targeted training.

Multimodal Large Language Models(MLLMs) have made rapid progress inspatial intelligence, yet existing spatial reasoning benchmarks largely assume pristine visual inputs and overlook the degradations that commonly occur in real-world deployment, such as motion blur, low light, adverse weather, lens distortion, and compression artifacts. This raises a fundamental question: how robust is thespatial intelligenceof current MLLMs when visual observations are imperfect? To answer this question, we introduce SpaceDG, the first large-scale dataset fordegradation-aware spatial understanding. It is constructed with a physically grounded degradation synthesis engine that embeds degradation formation process into3D Gaussian Splatting(3DGS) rendering, enabling realistic simulation of nine degradation types. The resulting dataset contains approximately 1M QA pairs from nearly 1,000 indoor scenes. We further introduce SpaceDG-Bench, anhuman-verified benchmarkwith 1,102 questions spanning 11 reasoning categories and 9visual degradationtypes, yielding over 10KVQA instances. Evaluating 25 open- and closed-source MLLMs reveals thatvisual degradations consistently and substantially impair spatial reasoning, exposing a critical robustness gap. Finally, we show thatfinetuningon SpaceDG markedly improves degradation robustness and can even surpass human performance under degraded conditions without any performance drop on clean images, highlighting the promise of degradation-aware training for robustspatial intelligence.

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