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^1 .

Acknowledgements
We would like to thank the expert annotators Josefina Manieu Seguel and Rinu Shaji Mariam, all anonymous workers on Amazon Mechanical Turk and the student volunteers (Akshit Sharma, Yue Ruan, Ali Gholami, Yasaman Etesam, Leon Kochiev, Sonia Raychaudhuri) at Simon Fraser University for their efforts in building the ScanRefer dataset, and Akshit Sharma for helping with statistics and figures. This work is funded by Google (AugmentedPerception), the ERC Starting Grant Scan2CAD (804724), and a Google Faculty Award. We would also like to thank the support of the TUM-IAS Rudolf M¨oßbauer and Hans Fischer Fellowships (Focus Group Visual Computing), as well as the the German Research Foundation (DFG) under the Grant Making Machine Learning on Static and Dynamic 3D Data Practical. Angel X. Chang is supported by the Canada CIFAR AI Chair program. Finally, we thank Angela Dai for the video voice-over.