Papers › Text2Loc: 3D Point Cloud Localization from Natural Language

Text2Loc: 3D Point Cloud Localization from Natural Language

27 Nov 2023CVPR 2024 1arXiv:2311.15977archive 2025-07-28

Yan Xia, Letian Shi, Zifeng Ding, João F. Henriques, Daniel Cremers

We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions and introduce a novel neural network, Text2Loc, that fully interprets the semantic relationship between points and text. Text2Loc follows a coarse-to-fine localization pipeline: text-submap global place recognition, followed by fine localization. In global place recognition, relational dynamics among each textual hint are captured in a hierarchical transformer with max-pooling (HTM), whereas a balance between positive and negative pairs is maintained using text-submap contrastive learning. Moreover, we propose a novel matching-free fine localization method to further refine the location predictions, which completely removes the need for complicated text-instance matching and is lighter, faster, and more accurate than previous methods. Extensive experiments show that Text2Loc improves the localization accuracy by up to 2× over the state-of-the-art on the KITTI360Pose dataset. Our project page is publicly available at \url{https://yan-xia.github.io/projects/text2loc/}.

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Code

kevin301342/cmmloc mentioned on GitHubpytorch report

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Tasks

Contrastive LearningVisual Place Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Visual Place Recognition KITTI360pose Text2Loc Localization Recall@1 0.37 #2 of 5 Archive leaderboard report

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Methods

HINT

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