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

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arxiv 1912.08830 v3 pith:XQKEW37V submitted 2019-12-18 cs.CV cs.CLcs.LGeess.IV

Dave Zhenyu Chen , Angel X. Chang , Matthias Nie{ss}ner This is my paper · ORCID
classification cs.CVcs.CLcs.LGeess.IV
keywords objectlanguagescanreferlocalizationnaturaldescriptionsdescriptorfused
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

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