検索して特定：大規模言語モデルとLiDAR幾何学を橋渡しする空間グラウンディング
Retrieve-to-Localize: Bridging Large Language Models and LiDAR Geometry for Spatial Grounding
LiDAR点群と言語モデルを組み合わせ、複雑な空間関係の質問に対して対象物の座標を高精度に特定する手法を提案。
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著者: Byounggun Park, Giyong Moon, Jusung Kim, Soonmin Hwang
分類: cs.CV, cs.RO
原文アブストラクト
LiDAR provides precise geometric information for spatial perception tasks such as object detection in autonomous driving and outdoor robotics. However, recognizing and localizing individual objects is not sufficient to answer questions that require composing spatial relations and grounding the intended target. Motivated by recent advances in large language models (LLMs) for autonomous driving, we leverage their language priors to interpret complex spatial questions and ground the referred target in LiDAR geometry. To support this spatial grounding capability, we introduce SpatialLiDAR-QA, which combines single- and multi-step relational grounding with complementary spatial understanding tasks. We further propose SpatialLiDAR-LM, which aligns LiDAR point features with an LLM and grounds target coordinates through language-conditioned, position-aware proposal retrieval and local point refinement. This design derives target coordinates directly from local LiDAR geometry rather than through textual language decoding. Experiments demonstrate substantial improvements over representative LiDAR--language models and multi-camera VLMs on precise coordinate prediction tasks. Our dataset and model training code will be publicly released.