PseudoGD: Enhancing Spatial Reasoning in Vision-Language Models through Pseudo Geometric Knowledge Distillation

ACL ARR 2026 January Submission9328 Authors

Keywords: Vision-Language Model, Spatial Reasoning, Spatial Understanding

Abstract: Recent Large Vision-Language Models (LVLMs) have shown remarkable success in general semantic understanding. However, they still struggle with 3D spatial reasoning tasks, such as estimating metric distances or understanding precise relative positions. Previous works, like SpatialVLM, tried to address this by using synthesized spatial VQA dataset. However, they are fundamentally limited because their vision encoders are biased toward 2D patterns learned from image-text pairs. In this paper, we argue that this lack of 3D awareness is a critical bottleneck that cannot be solved by data scaling alone. To address this, we propose Pseudo Geometric Distillation (PseudoGD), a framework designed to help vision encoders internalize 3D geometric information using only standard 2D images. PseudoGD explicitly injects metric scale and structural context into the encoder through a Joint Training strategy. This approach optimizes geometric learning and spatial VQA tasks together, ensuring that the Large Language Model (LLM) aligns well with the improved visual features in real-time. Extensive experiments on the OmniSpatial benchmark demonstrate that PseudoGD achieves State-of-the-Art (SOTA) performance across various model architectures. Notably, significant improvements in Hypothetical Perspective Taking and Locate tasks prove that our model has effectively learned a physical sense of space.

Paper Type: Short

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: vision question answering, cross-modal pretraining

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 9328