MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D
  Object Detection
Yuxue Yang Lue Fan Zhaoxiang Zhang
Label-efficient LiDAR-based 3D object detection is currently dominated by
weakly/semi-supervised methods. Instead of exclusively following one of them,
we propose MixSup, a more practical paradigm simultaneously utilizing massive
cheap coarse labels and a limited number of accurate labels for Mixed-grained
Supervision. We start by observing that point clouds are usually textureless,
making it hard to learn semantics. However, point clouds are geometrically rich
and scale-invariant to the distances from sensors, making it relatively easy to
learn the geometry of objects, such as poses and shapes. Thus, MixSup leverages
massive coarse cluster-level labels to learn semantics and a few expensive
box-level labels to learn accurate poses and shapes. We redesign the label
assignment in mainstream detectors, which allows them seamlessly integrated
into MixSup, enabling practicality and universality. We validate its
effectiveness in nuScenes, Waymo Open Dataset, and KITTI, employing various
detectors. MixSup achieves up to 97.31% of fully supervised performance, using
cheap cluster annotations and only 10% box annotations. Furthermore, we propose
PointSAM based on the Segment Anything Model for automated coarse labeling,
further reducing the annotation burden. The code is available at
https://github.com/BraveGroup/PointSAM-for-MixSup.