ROBOTICS
RESEARCH
Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
April 17, 2025
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.
AUTHORS
Written by
Ruslan Partsey
Ayush Jain
Ang Cao
Ishita Prasad
Aravind Rajeswaran
Abha Gejji
Ada Martin
Arjun Majumdar
Daniel Dugas
Franziska Meier
Krishna Murthy Jatavallabhula
Mikael Henaff
Mrinal Kalakrishnan
Nicolas Ballas
Oleksandr Maksymets
Paul McVay
Phillip Thomas
Alexander Sax
Sergio Arnaud
Vincent-Pierre Berges
Publisher
arXiv
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