iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point
  Clouds
Dongmin Choi Wonwoo Cho Kangyeol Kim Jaegul Choo
Accurately annotating multiple 3D objects in LiDAR scenes is laborious and
challenging. While a few previous studies have attempted to leverage
semi-automatic methods for cost-effective bounding box annotation, such methods
have limitations in efficiently handling numerous multi-class objects. To
effectively accelerate 3D annotation pipelines, we propose iDet3D, an efficient
interactive 3D object detector. Supporting a user-friendly 2D interface, which
can ease the cognitive burden of exploring 3D space to provide click
interactions, iDet3D enables users to annotate the entire objects in each scene
with minimal interactions. Taking the sparse nature of 3D point clouds into
account, we design a negative click simulation (NCS) to improve accuracy by
reducing false-positive predictions. In addition, iDet3D incorporates two click
propagation techniques to take full advantage of user interactions: (1) dense
click guidance (DCG) for keeping user-provided information throughout the
network and (2) spatial click propagation (SCP) for detecting other instances
of the same class based on the user-specified objects. Through our extensive
experiments, we present that our method can construct precise annotations in a
few clicks, which shows the practicality as an efficient annotation tool for 3D
object detection.