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arXiv · 2601.13132

SplatReasoner: Enhancing Embodied Reasoning and Grounding by Novel View Synthesis

Kim Yu-Ji·Dahye Lee·Kim Jun-Seong·Nam Hyeon-Woo·GeonU Kim·Yongjin Kwon·Yu-Chiang Frank Wang·Jaesung Choe·Tae-Hyun Oh
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Abstract

Vision-Language Models (VLMs) have demonstrated strong reasoning capabilities over images and videos, yet their application to embodied scene understanding often constrained by the fixed viewpoints stored in episodic RGB-D memories. These observations may fail to capture query-relevant evidence due to occlusions, object truncation, restricted fields of view, or suboptimal view composition. We present SplatReasoner, a framework that introduces novel view synthesis into the VLM reasoning process by leveraging 3D Gaussian Splatting (3DGS). Given a user query about a 3D scene, SplatReasoner retrieves relevant observations and synthesizes query-conditioned viewpoints that reveal the visual evidence needed to answer the query and ground the referred entities in 3D. Experiments show that query-conditioned novel view synthesis improves both embodied reasoning and 3D grounding over fixed-view memory and language-embedded 3DGS baselines.

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Kim Yu-Ji, Dahye Lee, Kim Jun-Seong, Nam Hyeon-Woo, GeonU Kim, Yongjin Kwon, Yu-Chiang Frank Wang, Jaesung Choe, Tae-Hyun Oh. 2026-01-19. SplatReasoner: Enhancing Embodied Reasoning and Grounding by Novel View Synthesis. https://arxiv.org/abs/2601.13132

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