  Multi-task networks can potentially improve performance and computational
efficiency compared to single-task networks, facilitating online deployment.
However, current multi-task architectures in point cloud perception combine
multiple task-specific point cloud representations, each requiring a separate
feature encoder and making the network structures bulky and slow. We propose
PAttFormer, an efficient multi-task architecture for joint semantic
segmentation and object detection in point clouds that only relies on a
point-based representation. The network builds on transformer-based feature
encoders using neighborhood attention and grid-pooling and a query-based
detection decoder using a novel 3D deformable-attention detection head design.
Unlike other LiDAR-based multi-task architectures, our proposed PAttFormer does
not require separate feature encoders for multiple task-specific point cloud
representations, resulting in a network that is 3x smaller and 1.4x faster
while achieving competitive performance on the nuScenes and KITTI benchmarks
for autonomous driving perception. Our extensive evaluations show substantial
gains from multi-task learning, improving LiDAR semantic segmentation by +1.7%
in mIou and 3D object detection by +1.7% in mAP on the nuScenes benchmark
compared to the single-task models.
