  DEtection TRansformer (DETR) started a trend that uses a group of learnable
queries for unified visual perception. This work begins by applying this
appealing paradigm to LiDAR-based point cloud segmentation and obtains a simple
yet effective baseline. Although the naive adaptation obtains fair results, the
instance segmentation performance is noticeably inferior to previous works. By
diving into the details, we observe that instances in the sparse point clouds
are relatively small to the whole scene and often have similar geometry but
lack distinctive appearance for segmentation, which are rare in the image
domain. Considering instances in 3D are more featured by their positional
information, we emphasize their roles during the modeling and design a robust
Mixed-parameterized Positional Embedding (MPE) to guide the segmentation
process. It is embedded into backbone features and later guides the mask
prediction and query update processes iteratively, leading to Position-Aware
Segmentation (PA-Seg) and Masked Focal Attention (MFA). All these designs impel
the queries to attend to specific regions and identify various instances. The
method, named Position-guided Point cloud Panoptic segmentation transFormer
(P3Former), outperforms previous state-of-the-art methods by 3.4% and 1.2% PQ
on SemanticKITTI and nuScenes benchmark, respectively. The source code and
models are available at https://github.com/SmartBot-PJLab/P3Former .
