  With the rapid proliferation of autonomous driving, there has been a
heightened focus on the research of lidar-based 3D semantic segmentation and
object detection methodologies, aiming to ensure the safety of traffic
participants. In recent decades, learning-based approaches have emerged,
demonstrating remarkable performance gains in comparison to conventional
algorithms. However, the segmentation and detection tasks have traditionally
been examined in isolation to achieve the best precision. To this end, we
propose an efficient multi-task learning framework named LiSD which can address
both segmentation and detection tasks, aiming to optimize the overall
performance. Our proposed LiSD is a voxel-based encoder-decoder framework that
contains a hierarchical feature collaboration module and a holistic information
aggregation module. Different integration methods are adopted to keep sparsity
in segmentation while densifying features for query initialization in
detection. Besides, cross-task information is utilized in an instance-aware
refinement module to obtain more accurate predictions. Experimental results on
the nuScenes dataset and Waymo Open Dataset demonstrate the effectiveness of
our proposed model. It is worth noting that LiSD achieves the state-of-the-art
performance of 83.3% mIoU on the nuScenes segmentation benchmark for lidar-only
methods.
