Informative Viewpoint Selection for Episodic-Memory Embodied Question Answering using Omnidirectional Images

Abstract:Embodied Question Answering (EQA) requires agents to answer natural language questions about surrounding environments from visual observations. In this work, we focus on open-vocabulary episodic-memory EQA (EM-EQA), where an agent answers free-form questions using recorded observation histories. Omnidirectional images are promising for this task, as they provide wide field-of-view observations that can capture surrounding context without requiring explicit camera rotations. However, omnidirectional images introduce two challenges for EQA: (i) equirectangular projection causes severe geometric distortion that degrades vision-language model (VLM) recognition accuracy, and (ii) feeding equirectangular images directly into VLMs introduces excessive irrelevant background information, reducing answer accuracy and increasing the visual-token burden. To address these challenges, we propose a viewpoint selection method for EM-EQA using omnidirectional images. Our method converts equirectangular observations into perspective views via cubemap projection, estimates question-conditioned relevance with fine-tuned BLIP-2, and selects informative and diverse viewpoints through diversity-aware greedy selection. Experiments on the Habitat-Matterport 3D (HM3D) subset of OpenEQA show that our method achieves state-of-the-art model performance among the reported model results with equirectangular observations. Moreover, after removing rotation views, which reduces observation frames by 65.5%, our method largely maintains its answer accuracy.

Accepted to ACCV 2026. Supplementary video is provided as an ancillary file

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2609.33288 [cs.CV]

(or arXiv:2609.33288v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.33288 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

ACCV2026_780_video.mp4

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