SSC3OD: Sparsely Supervised Collaborative 3D Object Detection from LiDAR
  Point Clouds
Yushan Han Hui Zhang Honglei Zhang Yidong Li
Collaborative 3D object detection, with its improved interaction advantage
among multiple agents, has been widely explored in autonomous driving. However,
existing collaborative 3D object detectors in a fully supervised paradigm
heavily rely on large-scale annotated 3D bounding boxes, which is
labor-intensive and time-consuming. To tackle this issue, we propose a sparsely
supervised collaborative 3D object detection framework SSC3OD, which only
requires each agent to randomly label one object in the scene. Specifically,
this model consists of two novel components, i.e., the pillar-based masked
autoencoder (Pillar-MAE) and the instance mining module. The Pillar-MAE module
aims to reason over high-level semantics in a self-supervised manner, and the
instance mining module generates high-quality pseudo labels for collaborative
detectors online. By introducing these simple yet effective mechanisms, the
proposed SSC3OD can alleviate the adverse impacts of incomplete annotations. We
generate sparse labels based on collaborative perception datasets to evaluate
our method. Extensive experiments on three large-scale datasets reveal that our
proposed SSC3OD can effectively improve the performance of sparsely supervised
collaborative 3D object detectors.