  Many basic indoor activities such as eating or writing are always conducted
upon different tabletops (e.g., coffee tables, writing desks). It is
indispensable to understanding tabletop scenes in 3D indoor scene parsing
applications. Unfortunately, it is hard to meet this demand by directly
deploying data-driven algorithms, since 3D tabletop scenes are rarely available
in current datasets. To remedy this defect, we introduce TO-Scene, a
large-scale dataset focusing on tabletop scenes, which contains 20,740 scenes
with three variants. To acquire the data, we design an efficient and scalable
framework, where a crowdsourcing UI is developed to transfer CAD objects from
ModelNet and ShapeNet onto tables from ScanNet, then the output tabletop scenes
are simulated into real scans and annotated automatically.
  Further, a tabletop-aware learning strategy is proposed for better perceiving
the small-sized tabletop instances. Notably, we also provide a real scanned
test set TO-Real to verify the practical value of TO-Scene. Experiments show
that the algorithms trained on TO-Scene indeed work on the realistic test data,
and our proposed tabletop-aware learning strategy greatly improves the
state-of-the-art results on both 3D semantic segmentation and object detection
tasks. Dataset and code are available at
https://github.com/GAP-LAB-CUHK-SZ/TO-Scene.
