  Unsupervised contrastive learning for indoor-scene point clouds has achieved
great successes. However, unsupervised learning point clouds in outdoor scenes
remains challenging because previous methods need to reconstruct the whole
scene and capture partial views for the contrastive objective. This is
infeasible in outdoor scenes with moving objects, obstacles, and sensors. In
this paper, we propose CO^3, namely Cooperative Contrastive Learning and
Contextual Shape Prediction, to learn 3D representation for outdoor-scene point
clouds in an unsupervised manner. CO^3 has several merits compared to existing
methods. (1) It utilizes LiDAR point clouds from vehicle-side and
infrastructure-side to build views that differ enough but meanwhile maintain
common semantic information for contrastive learning, which are more
appropriate than views built by previous methods. (2) Alongside the contrastive
objective, shape context prediction is proposed as pre-training goal and brings
more task-relevant information for unsupervised 3D point cloud representation
learning, which are beneficial when transferring the learned representation to
downstream detection tasks. (3) As compared to previous methods, representation
learned by CO^3 is able to be transferred to different outdoor scene dataset
collected by different type of LiDAR sensors. (4) CO^3 improves current
state-of-the-art methods on both Once and KITTI datasets by up to 2.58 mAP.
Codes and models will be released. We believe CO^3 will facilitate
understanding LiDAR point clouds in outdoor scene.
