Feeling the Space: Egomotion-Aware Video Representation for Efficient and Accurate 3D Scene Understanding
Recent Multimodal Large Language Models (MLLMs) have shown high potential for spatial reasoning within 3D scenes. However, they typically rely on computationally expensive 3D representations like point clouds or reconstructed Bird's-Eye View (BEV) maps, or lack physical grounding to resolve ambiguities in scale and size. This paper significantly enhances MLLMs with egomotion modality data, captured by Inertial Measurement Units (IMUs) concurrently with the video. In particular, we propose a novel framework, called Motion-MLLM, introducing two key components: (1) a cascaded motion-visual keyframe filtering module that leverages both IMU data and visual features to efficiently select a spar...
| Authors | Shuyao Shi, Kang G. Shin |
|---|---|
| Primary category | cs.CV |
| Published | 18 March 2026 |
| Last updated at source | 7 May 2026 |
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