Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D

• URL: http://arxiv.org/abs/2504.14151v1
• Date: Sat, 19 Apr 2025 02:51:24 GMT
• Title: Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
• Authors: Sergio Arnaud, Paul McVay, Ada Martin, Arjun Majumdar, Krishna Murthy Jatavallabhula, Phillip Thomas, Ruslan Partsey, Daniel Dugas, Abha Gejji, Alexander Sax, Vincent-Pierre Berges, Mikael Henaff, Ayush Jain, Ang Cao, Ishita Prasad, Mrinal Kalakrishnan, Michael Rabbat, Nicolas Ballas, Mido Assran, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier,
• Abstract summary: LOCATE 3D is a model for localizing objects in 3D scenes from referring expressions like "the small coffee table between the sofa and the lamp"<n>It operates directly on sensor observation streams (posed RGB-D frames), enabling real-world deployment on robots and AR devices.
• Score: 68.23391872643268
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present LOCATE 3D, a model for localizing objects in 3D scenes from referring expressions like "the small coffee table between the sofa and the lamp." LOCATE 3D sets a new state-of-the-art on standard referential grounding benchmarks and showcases robust generalization capabilities. Notably, LOCATE 3D operates directly on sensor observation streams (posed RGB-D frames), enabling real-world deployment on robots and AR devices. Key to our approach is 3D-JEPA, a novel self-supervised learning (SSL) algorithm applicable to sensor point clouds. It takes as input a 3D pointcloud featurized using 2D foundation models (CLIP, DINO). Subsequently, masked prediction in latent space is employed as a pretext task to aid the self-supervised learning of contextualized pointcloud features. Once trained, the 3D-JEPA encoder is finetuned alongside a language-conditioned decoder to jointly predict 3D masks and bounding boxes. Additionally, we introduce LOCATE 3D DATASET, a new dataset for 3D referential grounding, spanning multiple capture setups with over 130K annotations. This enables a systematic study of generalization capabilities as well as a stronger model.

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