Label-Efficient 3D Object Detection For Road-Side Units
Minh-Quan Dao Holger Caesar Julie Stephany Berrio Mao Shan Stewart Worrall Vincent Frémont Ezio Malis
Occlusion presents a significant challenge for safety-critical applications
such as autonomous driving. Collaborative perception has recently attracted a
large research interest thanks to the ability to enhance the perception of
autonomous vehicles via deep information fusion with intelligent roadside units
(RSU), thus minimizing the impact of occlusion. While significant advancement
has been made, the data-hungry nature of these methods creates a major hurdle
for their real-world deployment, particularly due to the need for annotated RSU
data. Manually annotating the vast amount of RSU data required for training is
prohibitively expensive, given the sheer number of intersections and the effort
involved in annotating point clouds. We address this challenge by devising a
label-efficient object detection method for RSU based on unsupervised object
discovery. Our paper introduces two new modules: one for object discovery based
on a spatial-temporal aggregation of point clouds, and another for refinement.
Furthermore, we demonstrate that fine-tuning on a small portion of annotated
data allows our object discovery models to narrow the performance gap with, or
even surpass, fully supervised models. Extensive experiments are carried out in
simulated and real-world datasets to evaluate our method.