  We present a hybrid-view-based knowledge distillation framework, termed
HVDistill, to guide the feature learning of a point cloud neural network with a
pre-trained image network in an unsupervised manner. By exploiting the
geometric relationship between RGB cameras and LiDAR sensors, the
correspondence between the two modalities based on both image-plane view and
bird-eye view can be established, which facilitates representation learning.
Specifically, the image-plane correspondences can be simply obtained by
projecting the point clouds, while the bird-eye-view correspondences can be
achieved by lifting pixels to the 3D space with the predicted depths under the
supervision of projected point clouds. The image teacher networks provide rich
semantics from the image-plane view and meanwhile acquire geometric information
from the bird-eye view. Indeed, image features from the two views naturally
complement each other and together can ameliorate the learned feature
representation of the point cloud student networks. Moreover, with a
self-supervised pre-trained 2D network, HVDistill requires neither 2D nor 3D
annotations. We pre-train our model on nuScenes dataset and transfer it to
several downstream tasks on nuScenes, SemanticKITTI, and KITTI datasets for
evaluation. Extensive experimental results show that our method achieves
consistent improvements over the baseline trained from scratch and
significantly outperforms the existing schemes. Codes are available at
git@github.com:zhangsha1024/HVDistill.git.
