  In advanced paradigms of autonomous driving, learning Bird's Eye View (BEV)
representation from surrounding views is crucial for multi-task framework.
However, existing methods based on depth estimation or camera-driven attention
are not stable to obtain transformation under noisy camera parameters, mainly
with two challenges, accurate depth prediction and calibration. In this work,
we present a completely Multi-Camera Calibration Free Transformer (CFT) for
robust BEV representation, which focuses on exploring implicit mapping, not
relied on camera intrinsics and extrinsics. To guide better feature learning
from image views to BEV, CFT mines potential 3D information in BEV via our
designed position-aware enhancement (PA). Instead of camera-driven point-wise
or global transformation, for interaction within more effective region and
lower computation cost, we propose a view-aware attention which also reduces
redundant computation and promotes converge. CFT achieves 49.7% NDS on the
nuScenes detection task leaderboard, which is the first work removing camera
parameters, comparable to other geometry-guided methods. Without temporal input
and other modal information, CFT achieves second highest performance with a
smaller image input 1600 * 640. Thanks to view-attention variant, CFT reduces
memory and transformer FLOPs for vanilla attention by about 12% and 60%,
respectively, with improved NDS by 1.0%. Moreover, its natural robustness to
noisy camera parameters makes CFT more competitive.
