  Bird's-Eye-View (BEV) 3D Object Detection is a crucial multi-view technique
for autonomous driving systems. Recently, plenty of works are proposed,
following a similar paradigm consisting of three essential components, i.e.,
camera feature extraction, BEV feature construction, and task heads. Among the
three components, BEV feature construction is BEV-specific compared with 2D
tasks. Existing methods aggregate the multi-view camera features to the
flattened grid in order to construct the BEV feature. However, flattening the
BEV space along the height dimension fails to emphasize the informative
features of different heights. For example, the barrier is located at a low
height while the truck is located at a high height. In this paper, we propose a
novel method named BEV Slice Attention Network (BEV-SAN) for exploiting the
intrinsic characteristics of different heights. Instead of flattening the BEV
space, we first sample along the height dimension to build the global and local
BEV slices. Then, the features of BEV slices are aggregated from the camera
features and merged by the attention mechanism. Finally, we fuse the merged
local and global BEV features by a transformer to generate the final feature
map for task heads. The purpose of local BEV slices is to emphasize informative
heights. In order to find them, we further propose a LiDAR-guided sampling
strategy to leverage the statistical distribution of LiDAR to determine the
heights of local slices. Compared with uniform sampling, LiDAR-guided sampling
can determine more informative heights. We conduct detailed experiments to
demonstrate the effectiveness of BEV-SAN. Code will be released.
