  3D object detection using point clouds has attracted increasing attention due
to its wide applications in autonomous driving and robotics. However, most
existing studies focus on single point cloud frames without harnessing the
temporal information in point cloud sequences. In this paper, we design
TransPillars, a novel transformer-based feature aggregation technique that
exploits temporal features of consecutive point cloud frames for multi-frame 3D
object detection. TransPillars aggregates spatial-temporal point cloud features
from two perspectives. First, it fuses voxel-level features directly from
multi-frame feature maps instead of pooled instance features to preserve
instance details with contextual information that are essential to accurate
object localization. Second, it introduces a hierarchical coarse-to-fine
strategy to fuse multi-scale features progressively to effectively capture the
motion of moving objects and guide the aggregation of fine features. Besides, a
variant of deformable transformer is introduced to improve the effectiveness of
cross-frame feature matching. Extensive experiments show that our proposed
TransPillars achieves state-of-art performance as compared to existing
multi-frame detection approaches. Code will be released.
