JAEGER: Joint 3D Audio-Visual Grounding and Reasoning in Simulated Physical Environments

• URL: http://arxiv.org/abs/2602.18527v1
• Date: Fri, 20 Feb 2026 04:06:07 GMT
• Title: JAEGER: Joint 3D Audio-Visual Grounding and Reasoning in Simulated Physical Environments
• Authors: Zhan Liu, Changli Tang, Yuxin Wang, Zhiyuan Zhu, Youjun Chen, Yiwen Shao, Tianzi Wang, Lei Ke, Zengrui Jin, Chao Zhang,
• Abstract summary: We present JAEGER, a framework that extends AV-LLMs to 3D space to enable joint spatial grounding and reasoning.<n>A core contribution of our work is the neural intensity vector (Neural IV), a learned spatial audio representation.<n>Our approach consistently surpasses 2D-centric baselines across diverse spatial perception and reasoning tasks.
• Score: 34.02990381039783
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current audio-visual large language models (AV-LLMs) are predominantly restricted to 2D perception, relying on RGB video and monaural audio. This design choice introduces a fundamental dimensionality mismatch that precludes reliable source localization and spatial reasoning in complex 3D environments. We address this limitation by presenting JAEGER, a framework that extends AV-LLMs to 3D space, to enable joint spatial grounding and reasoning through the integration of RGB-D observations and multi-channel first-order ambisonics. A core contribution of our work is the neural intensity vector (Neural IV), a learned spatial audio representation that encodes robust directional cues to enhance direction-of-arrival estimation, even in adverse acoustic scenarios with overlapping sources. To facilitate large-scale training and systematic evaluation, we propose SpatialSceneQA, a benchmark of 61k instruction-tuning samples curated from simulated physical environments. Extensive experiments demonstrate that our approach consistently surpasses 2D-centric baselines across diverse spatial perception and reasoning tasks, underscoring the necessity of explicit 3D modelling for advancing AI in physical environments. Our source code, pre-trained model checkpoints and datasets will be released upon acceptance.

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