  LiDAR-camera 3D representation pretraining has shown significant promise for
3D perception tasks and related applications. However, two issues widely exist
in this framework: 1) Solely keyframes are used for training. For example, in
nuScenes, a substantial quantity of unpaired LiDAR and camera frames remain
unutilized, limiting the representation capabilities of the pretrained network.
2) The contrastive loss erroneously distances points and image regions with
identical semantics but from different frames, disturbing the semantic
consistency of the learned presentations. In this paper, we propose a novel
Vision-Foundation-Model-driven sample exploring module to meticulously select
LiDAR-Image pairs from unexplored frames, enriching the original training set.
We utilized timestamps and the semantic priors from VFMs to identify
well-synchronized training pairs and to discover samples with diverse content.
Moreover, we design a cross- and intra-modal conflict-aware contrastive loss
using the semantic mask labels of VFMs to avoid contrasting semantically
similar points and image regions. Our method consistently outperforms existing
state-of-the-art pretraining frameworks across three major public autonomous
driving datasets: nuScenes, SemanticKITTI, and Waymo on 3D semantic
segmentation by +3.0\%, +3.0\%, and +3.3\% in mIoU, respectively. Furthermore,
our approach exhibits adaptable generalization to different 3D backbones and
typical semantic masks generated by non-VFM models.
