2DDATA: 2D Detection Annotations Transmittable Aggregation for Semantic
  Segmentation on Point Cloud
Guan-Cheng Lee
Recently, multi-modality models have been introduced because of the
complementary information from different sensors such as LiDAR and cameras. It
requires paired data along with precise calibrations for all modalities, the
complicated calibration among modalities hugely increases the cost of
collecting such high-quality datasets, and hinder it from being applied to
practical scenarios. Inherit from the previous works, we not only fuse the
information from multi-modality without above issues, and also exhaust the
information in the RGB modality. We introduced the 2D Detection Annotations
Transmittable Aggregation(\textbf{2DDATA}), designing a data-specific branch,
called \textbf{Local Object Branch}, which aims to deal with points in a
certain bounding box, because of its easiness of acquiring 2D bounding box
annotations. We demonstrate that our simple design can transmit bounding box
prior information to the 3D encoder model, proving the feasibility of large
multi-modality models fused with modality-specific data.