Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching
For researchers deploying MLLMs in physical environments requiring spatial reasoning, this work provides a benchmark and training method to address a critical bottleneck.
Current MLLMs struggle with wide-baseline matching (WBM) for spatial reasoning; the proposed ReasonMatch-Bench reveals a large gap (human 84.0 F1 vs. best baseline 37.2 on a hard subset). The authors introduce DCRL training, which improves WBM performance and transfers to related spatial benchmarks while maintaining general visual understanding.
Wide-baseline matching (WBM) requires integrating geometric understanding, viewpoint changes, fine-grained perception, and occlusion reasoning, making it a challenging testbed for spatial reasoning in multimodal large language models (MLLMs) deployed in physical environments. However, current MLLMs lack systematic evaluation and training frameworks for these capabilities. We introduce ReasonMatch-Bench, a benchmark stratified by viewpoint displacement and matching granularity across indoor, outdoor, and object-centric scenarios, and show that current MLLMs still struggle with fine-grained wide-baseline correspondence: on a difficult 90-sample subset, human annotators achieve 84.0 F1, while the best existing baseline reaches 37.2. To bridge this gap, we build a scalable data-generation pipeline that automatically extracts wide-baseline view pairs from large-scale video-3D corpora, including RGB-D videos and SfM reconstructions, yielding diverse and verifiable supervision. We further propose Dynamic Correspondence Reinforcement Learning (DCRL), which combines Image-Level Viewpoint Progression and Point-Level Correspondence Curriculum to improve WBM training through verifiable rewards without explicit CoT supervision. Extensive experiments show that DCRL substantially improves ReasonMatch-Bench and transfers to related spatial benchmarks, while maintaining general visual understanding performance with modest gains on several benchmarks.