ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language
European Conference on Computer Vision (ECCV), 2020.
Dave Zhenyu Chen1 Angel X. Chang2 Matthias Nießner1
1Technical University of Munich 2Simon Fraser University
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Introduction
We introduce the task of 3D object localization in RGB-D scans using natural language descriptions. As input, we assume a point cloud of a scanned 3D scene along with a free-form description of a specified target object. To address this task, we propose ScanRefer, learning a fused descriptor from 3D object proposals and encoded sentence embeddings. This fused descriptor correlates language expressions with geometric features, enabling regression of the 3D bounding box of a target object. We also introduce the ScanRefer dataset, containing 51,583 descriptions of 11,046 objects from 800 ScanNet scenes. ScanRefer is the first large-scale effort to perform object localization via natural language expression directly in 3D.
Video
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Publication
European Conference on Computer Vision (ECCV), 2020.Paper | arXiv | Code
If you find our project useful, please consider citing us:
@article{chen2020scanrefer,
title={ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language},
author={Chen, Dave Zhenyu and Chang, Angel X and Nie{\ss}ner, Matthias},
journal={16th European Conference on Computer Vision (ECCV)},
year={2020}
}