Newest papers

RecallGS: Geometry-Grounded Recall for Online Semantic Gaussian Splatting

Thanh-Khoi Nguyen, Thien-Phuc Tran, Minh-Triet Tran · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31293 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

オンラインセグメント3D Gaussian Splatting (3DGS)はストリーミング観測からオープン語彙のシーン理解を可能にします。 しかし、既存の方法は、追加的に学習した3Dネットワークを使用して画像由来のセマンティック機能から欠けている地理的文脈を回復し、余分なトレーニングとランタイムオーバーヘッドを発生させます。我々は、そのような地理的-セマンティックの証拠がすでにエージェントの観測履歴に存在し、学習したよりも回収することができることを観察します。この洞察に基づいて、我々はRecallGS(オンラインセマンティック Gaussian Splattingのための地理基盤回収)を導入し、過去の観測を非パラメーメ

Machine translation (Meta M2M100); the English original follows.

Abstract

Online semantic 3D Gaussian Splatting (3DGS) enables open-vocabulary scene understanding from streaming observations. However, existing methods recover geometric context missing from image-derived semantic features using an additionally learned 3D network, incurring extra training and runtime overhead. We observe that such geometric-semantic evidence may already exist in the agent's observation history and can be retrieved rather than learned. Based on this insight, we introduce RecallGS (Geometry-Grounded Recall for Online Semantic Gaussian Splatting), a lightweight, training-free mechanism that treats past observations as a non-parametric geometric-semantic prior. RecallGS follows a recall-ground-fuse pipeline: CLIP retrieves relevant observations, alpha-compositing weights identify geometrically supported views, and aggregate their semantic features over each Gaussian's rendered footprint. On ScanNet and ScanNet++, RecallGS achieves strong semantic segmentation performance at 2.48 FPS, demonstrating that geometric-semantic context can be effectively recovered from observation history without an additional learned 3D branch.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Artificial intelligence, Context (archaeology), Segmentation, Recall, Gaussian

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange