  In contrast to extensive studies on general vision, pre-training for scalable
visual autonomous driving remains seldom explored. Visual autonomous driving
applications require features encompassing semantics, 3D geometry, and temporal
information simultaneously for joint perception, prediction, and planning,
posing dramatic challenges for pre-training. To resolve this, we bring up a new
pre-training task termed as visual point cloud forecasting - predicting future
point clouds from historical visual input. The key merit of this task captures
the synergic learning of semantics, 3D structures, and temporal dynamics. Hence
it shows superiority in various downstream tasks. To cope with this new
problem, we present ViDAR, a general model to pre-train downstream visual
encoders. It first extracts historical embeddings by the encoder. These
representations are then transformed to 3D geometric space via a novel Latent
Rendering operator for future point cloud prediction. Experiments show
significant gain in downstream tasks, e.g., 3.1% NDS on 3D detection, ~10%
error reduction on motion forecasting, and ~15% less collision rate on
planning.
