オープンボキャブラリなシーン地図構築のためのシーン言語モデル
A Scene Language Model for Open-Vocabulary Scene Mapping
シーン全体を構造化テキストの物体リストとして保持し、画像入力ごとに追加・編集・削除で地図を更新するSceneLMを提案。専用モジュール構成の従来手法と同等性能を6〜12倍コンパクトな表現で実現し、四足歩行ロボット上でのオンライン動作も示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Adam Lilja, Fabio Hübel, Siming He, Junsheng Fu, Claire Tomlin, Lars Hammarstrand, Jitendra Malik, Jonas Frey, Marco Pavone
分類: cs.CV, cs.RO
原文アブストラクト
Open-vocabulary 3D scene mapping aims to build a persistent representation of the objects in an environment. Existing systems typically rely on engineered mapping pipelines to associate observations, merge information across views, and maintain a consistent scene representation over time. Many additionally store feature-rich object representations, such as embeddings or image crops, increasing the size and complexity of the persistent memory. We introduce SceneLM, a Scene-Language Model that directly maintains a textual scene map. The full scene is represented as a structured text list of objects, which serves as the model's only persistent memory. For each input image, the model reads the current scene state and updates the map by adding, editing, and removing objects. To learn this behavior, we introduce supervision tasks for iterative scene map maintenance together with an automatic annotation pipeline that generates training data from images without human labels. We evaluate SceneLM on both a language-grounded retrieval benchmark and a localization benchmark. Across both benchmarks, the model produces a scene map that achieves competitive performance with complete mapping systems built from dedicated perception and geometric modules while producing a scene representation that is 6-12x more compact. We further show that SceneLM can be run online on an edge device through experiments on a quadruped. These results show that a persistent open-vocabulary 3D scene map can be maintained directly by a single vision-language model using only a lightweight text representation. Training and inference code is available on https://goldengait.github.io/scenelm/.