GSMem: 3Dガウススプラッティングを永続的空間記憶として用いたゼロショット身体化探索と推論
GSMem: 3D Gaussian Splatting as Persistent Spatial Memory for Zero-Shot Embodied Exploration and Reasoning
3Dガウススプラッティングを永続的な空間記憶として活用し、エージェントが初期観察で見逃した対象を後から再観察できるようにする、ゼロショットの身体化探索・推論フレームワークを提案した。
詳しい要約
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著者: Yiren Lu, Yi Du, Disheng Liu, Yunlai Zhou, Chen Wang, Yu Yin
分類: cs.CV, cs.RO
原文アブストラクト
Effective embodied exploration requires agents to accumulate and retain spatial knowledge over time. However, existing scene representations, such as discrete scene graphs or static view-based snapshots, lack \textit{post-hoc re-observability}. If an initial observation misses a target, the resulting memory omission is often irrecoverable. To bridge this gap, we propose \textbf{GSMem}, a zero-shot embodied exploration and reasoning framework built upon 3D Gaussian Splatting (3DGS). By explicitly parameterizing continuous geometry and dense appearance, 3DGS serves as a persistent spatial memory that endows the agent with \textit{Spatial Recollection}: the ability to render photorealistic novel views from optimal, previously unoccupied viewpoints. To operationalize this, GSMem employs a retrieval mechanism that simultaneously leverages parallel object-level scene graphs and semantic-level language fields. This complementary design robustly localizes target regions, enabling the agent to ``hallucinate'' optimal views for high-fidelity Vision-Language Model (VLM) reasoning. Furthermore, we introduce a hybrid exploration strategy that combines VLM-driven semantic scoring with a 3DGS-based coverage objective, balancing task-aware exploration with geometric coverage. Extensive experiments on embodied question answering and lifelong navigation demonstrate the robustness and effectiveness of our framework
関連論文
- 不整備環境における3Dリアクティブ制御とフロンティア探索探索/ナビゲーション
