LiDAR panoptic segmentation facilitates an autonomous vehicle to
comprehensively understand the surrounding objects and scenes and is required
to run in real time. The recent proposal-free methods accelerate the algorithm,
but their effectiveness and efficiency are still limited owing to the
difficulty of modeling non-existent instance centers and the costly
center-based clustering modules. To achieve accurate and real-time LiDAR
panoptic segmentation, a novel center focusing network (CFNet) is introduced.
Specifically, the center focusing feature encoding (CFFE) is proposed to
explicitly understand the relationships between the original LiDAR points and
virtual instance centers by shifting the LiDAR points and filling in the center
points. Moreover, to leverage the redundantly detected centers, a fast center
deduplication module (CDM) is proposed to select only one center for each
instance. Experiments on the SemanticKITTI and nuScenes panoptic segmentation
benchmarks demonstrate that our CFNet outperforms all existing methods by a
large margin and is 1.6 times faster than the most efficient method. The code
is available at this https URL

为了实现准确且实时的 LiDAR 全景分割，引入了一种新的中心聚焦网络（CFNet），其中包括了中心聚焦特征编码（CFFE）和快速中心去重模块（CDM），在 SemanticKITTI 和 nuScenes 全景分割基准测试中性能优于现有方法，并且比效率最高的方法快 1.6 倍。

面向实时激光雷达全景分割的中心聚焦网络

Center Focusing Network for Real-Time LiDAR Panoptic Segmentation

State-of-the-art lidar panoptic segmentation (LPS) methods follow bottom-up
segmentation-centric fashion wherein they build upon semantic segmentation
networks by utilizing clustering to obtain object instances. In this paper, we
re-think this approach and propose a surprisingly simple yet effective
detection-centric network for both LPS and tracking. Our network is modular by
design and optimized for all aspects of both the panoptic segmentation and
tracking task. One of the core components of our network is the object instance
detection branch, which we train using point-level (modal) annotations, as
available in segmentation-centric datasets. In the absence of amodal (cuboid)
annotations, we regress modal centroids and object extent using
trajectory-level supervision that provides information about object size, which
cannot be inferred from single scans due to occlusions and the sparse nature of
the lidar data. We obtain fine-grained instance segments by learning to
associate lidar points with detected centroids. We evaluate our method on
several 3D/4D LPS benchmarks and observe that our model establishes a new
state-of-the-art among open-sourced models, outperforming recent query-based
models.

本文提出了一个简单而有效的基于检测的网络，用于激光雷达全景分割和追踪任务，通过使用点级别注释训练目标实例检测分支，以及使用轨迹级别监督回归模态中心和物体范围来获取细粒度实例段，该方法在多个 3D/4D LPS 基准测试中表现出色，超过最新的基于查询的模型，建立了新的开源模型的最新技术水平。