RSI-LGS: Recursive Self-Improvement for Language Gaussian Splatting

Open-vocabulary 3D querying aims to localize and identify objects in 3D scenes according to arbitrary natural language prompts. Existing methods typically adopt a one-stage pipeline that aligns 3D scene representations with vision-language semantics in a static manner. Consequently, these models lack effective mechanisms for diagnosing and rectifying erroneous predictions. To address this limitation, we propose RSI-LGS, an open-vocabulary 3D querying framework equipped with a pipeline-level self-improvement loop. RSI-LGS reviews intermediate query results to diagnose localization and semantic matching errors, and adaptively adjusts the querying pipeline for subsequent iterations. This enables recursive refinement of the inference strategy and improves 3D open-vocabulary query results.