PARC-Loc: Text-to-Point-Cloud Localization with Partial Assignment and Relational Consistency
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2610.09761 (cs)
[Submitted on 7 Oct 2026]
Title:PARC-Loc: Text-to-Point-Cloud Localization with Partial Assignment and Relational Consistency
View a PDF of the paper titled PARC-Loc: Text-to-Point-Cloud Localization with Partial Assignment and Relational Consistency, by Shengkai Ma and 2 other authors
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Abstract:Text-to-point-cloud localization estimates a position in a city-scale 3D map from descriptions of surrounding objects. Existing coarse-to-fine methods retrieve submaps using aggregate learned compatibility and then localize within a selected submap. However, repetitive or similar urban objects can inflate the embedding similarity between the query and multiple submaps, even when the instance layout within a submap violates the query description. Meanwhile, query-relevant instances often span submap boundaries, leaving the retrieved submap with incomplete contextual evidence. We term these failure modes layout-inconsistent aliasing and boundary evidence incompleteness, respectively. To address them, we propose PARC-Loc, a coarse-to-fine localization framework built on Partial Assignment with Relational Consistency (PARC). PARC jointly models hint-object compatibility and pairwise spatial relations, allowing unmatched elements while favoring assignments consistent with the queried layout. At the coarse stage, its candidate-level assessment complements neural similarity for layout-consistent submap selection. At the fine stage, the context is expanded with query-relevant instances from adjacent submaps, while PARC yields object-level matching weights that guide cross-modal attention. Extensive experiments on KITTI360Pose and CityLoc show that PARC-Loc outperforms conventional coarse-to-fine baselines. On KITTI360Pose, our method improves Top-1 localization recall at 5 m from 0.50 to 0.67, achieving a 34% relative gain over the strongest baseline.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.09761 [cs.CV] |
| (or arXiv:2610.09761v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09761
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled PARC-Loc: Text-to-Point-Cloud Localization with Partial Assignment and Relational Consistency, by Shengkai Ma and 2 other authors
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