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

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 [8] scenes. ScanRefer is the first large-scale effort to perform object localization via natural language expression directly in 3D (Code: https://daveredrum.github.io/ScanRefer/).