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ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language

2020-07-21 21:41:53
Dave Zhenyu Chen, Angel X. Chang, Matthias Nießner

Abstract

tract: 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.

Abstract (translated)

URL

https://arxiv.org/abs/1912.08830

PDF

https://arxiv.org/pdf/1912.08830


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