  Manually annotating 3D point clouds is laborious and costly, limiting the
training data preparation for deep learning in real-world object detection.
While a few previous studies tried to automatically generate 3D bounding boxes
from weak labels such as 2D boxes, the quality is sub-optimal compared to human
annotators. This work proposes a novel autolabeler, called multimodal attention
point generator (MAP-Gen), that generates high-quality 3D labels from weak 2D
boxes. It leverages dense image information to tackle the sparsity issue of 3D
point clouds, thus improving label quality. For each 2D pixel, MAP-Gen predicts
its corresponding 3D coordinates by referencing context points based on their
2D semantic or geometric relationships. The generated 3D points densify the
original sparse point clouds, followed by an encoder to regress 3D bounding
boxes. Using MAP-Gen, object detection networks that are weakly supervised by
2D boxes can achieve 94~99% performance of those fully supervised by 3D
annotations. It is hopeful this newly proposed MAP-Gen autolabeling flow can
shed new light on utilizing multimodal information for enriching sparse point
clouds.
