{"ID":5937074,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T13:28:50.14143324Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05077","arxiv_id":"2607.05077","title":"LangLoc: \"Tell Me What You See\"","abstract":"We tackle fine-grained indoor localization from natural language: given a free-form description of one's surroundings, estimate the observer's 2D position and heading within a known 3D environment. Language queries are lightweight, privacy-preserving, and need no camera - yet prior work stops at coarse scene retrieval and cannot resolve an intra-scene pose. We close this gap with LangLoc, a three-stage pipeline that (i) retrieves the correct scene via a dual-branch GATv2 encoder with CLIP semantic features, surpassing the previous best by 8 percentage points in Top-1 recall; (ii) estimates position and heading by scoring a dense floor grid through ray-cast object visibility, reaching a median error of 0.95 m; and (iii) resolves residual ambiguity through a Bayesian dialog module that asks targeted yes/no questions and updates a pose posterior until the location is pinpointed. To support this task we contribute a benchmark of $13{,}000{+}$ pose-indexed natural-language descriptions over $1{,}300{+}$ indoor 3D scans.","short_abstract":"We tackle fine-grained indoor localization from natural language: given a free-form description of one's surroundings, estimate the observer's 2D position and heading within a known 3D environment. Language queries are lightweight, privacy-preserving, and need no camera - yet prior work stops at coarse scene retrieval...","url_abs":"https://arxiv.org/abs/2607.05077","url_pdf":"https://arxiv.org/pdf/2607.05077v1","authors":"[\"Shaurya Kishore Panwar\",\"Roham Zendehdel Nobari\",\"Shirley Feng Yi Lau\",\"Abu Bakr Rahman Shaik\",\"Manuel Günther\",\"Marc Pollefeys\",\"Daniel Barath\"]","published":"2026-07-06T13:39:22Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}