Paper page - SpaceDG: Benchmarking Spatial Intelligence under Visual Degradation

Source: https://huggingface.co/papers/2605.22536 Authors:

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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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