Are Dense Labels Always Necessary for 3D Object Detection from Point
  Cloud?
Chenqiang Gao Chuandong Liu Jun Shu Fangcen Liu Jiang Liu Luyu Yang Xinbo Gao Deyu Meng
Current state-of-the-art (SOTA) 3D object detection methods often require a
large amount of 3D bounding box annotations for training. However, collecting
such large-scale densely-supervised datasets is notoriously costly. To reduce
the cumbersome data annotation process, we propose a novel sparsely-annotated
framework, in which we just annotate one 3D object per scene. Such a sparse
annotation strategy could significantly reduce the heavy annotation burden,
while inexact and incomplete sparse supervision may severely deteriorate the
detection performance. To address this issue, we develop the SS3D++ method that
alternatively improves 3D detector training and confident fully-annotated scene
generation in a unified learning scheme. Using sparse annotations as seeds, we
progressively generate confident fully-annotated scenes based on designing a
missing-annotated instance mining module and reliable background mining module.
Our proposed method produces competitive results when compared with SOTA
weakly-supervised methods using the same or even more annotation costs.
Besides, compared with SOTA fully-supervised methods, we achieve on-par or even
better performance on the KITTI dataset with about 5x less annotation cost, and
90% of their performance on the Waymo dataset with about 15x less annotation
cost. The additional unlabeled training scenes could further boost the
performance. The code will be available at https://github.com/gaocq/SS3D2.