  3D object detection is a core perceptual challenge for robotics and
autonomous driving. However, the class-taxonomies in modern autonomous driving
datasets are significantly smaller than many influential 2D detection datasets.
In this work, we address the long-tail problem by leveraging both the large
class-taxonomies of modern 2D datasets and the robustness of state-of-the-art
2D detection methods. We proceed to mine a large, unlabeled dataset of images
and LiDAR, and estimate 3D object bounding cuboids, seeded from an
off-the-shelf 2D instance segmentation model. Critically, we constrain this
ill-posed 2D-to-3D mapping by using high-definition maps and object size
priors. The result of the mining process is 3D cuboids with varying confidence.
This mining process is itself a 3D object detector, although not especially
accurate when evaluated as such. However, we then train a 3D object detection
model on these cuboids, consistent with other recent observations in the deep
learning literature, we find that the resulting model is fairly robust to the
noisy supervision that our mining process provides. We mine a collection of
1151 unlabeled, multimodal driving logs from an autonomous vehicle and use the
discovered objects to train a LiDAR-based object detector. We show that
detector performance increases as we mine more unlabeled data. With our full,
unlabeled dataset, our method performs competitively with fully supervised
methods, even exceeding the performance for certain object categories, without
any human 3D annotations.
