Panoptic segmentation as an integrated task of both static environmental
understanding and dynamic object identification, has recently begun to receive
broad research interest. In this paper, we propose a new computationally
efficient LiDAR based panoptic segmentation framework, called GP-S3Net.
GP-S3Net is a proposal-free approach in which no object proposals are needed to
identify the objects in contrast to conventional two-stage panoptic systems,
where a detection network is incorporated for capturing instance information.
Our new design consists of a novel instance-level network to process the
semantic results by constructing a graph convolutional network to identify
objects (foreground), which later on are fused with the background classes.
Through the fine-grained clusters of the foreground objects from the semantic
segmentation backbone, over-segmentation priors are generated and subsequently
processed by 3D sparse convolution to embed each cluster. Each cluster is
treated as a node in the graph and its corresponding embedding is used as its
node feature. Then a GCNN predicts whether edges exist between each cluster
pair. We utilize the instance label to generate ground truth edge labels for
each constructed graph in order to supervise the learning. Extensive
experiments demonstrate that GP-S3Net outperforms the current state-of-the-art
approaches, by a significant margin across available datasets such as, nuScenes
and SemanticPOSS, ranking first on the competitive public SemanticKITTI
leaderboard upon publication.

本文提出了一种高效的基于 LiDAR 的全景分割框架 GP-S3Net，通过构建图卷积网络以识别物体并将其与背景类融合，利用实例标签为构造的每个图生成地面真实边缘标签来监督学习，在 nuScenes 和 SemanticPOSS 等数据集上实验表明 GP-S3Net 优于现有最先进的方法，在公共 SemanticKITTI 排行榜上排名第一。

基于图的全景稀疏语义分割网络 GP-S3Net

GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network

We introduce Spatial Group Convolution (SGC) for accelerating the computation
of 3D dense prediction tasks. SGC is orthogonal to group convolution, which
works on spatial dimensions rather than feature channel dimension. It divides
input voxels into different groups, then conducts 3D sparse convolution on
these separated groups. As only valid voxels are considered when performing
convolution, computation can be significantly reduced with a slight loss of
accuracy. The proposed operations are validated on semantic scene completion
task, which aims to predict a complete 3D volume with semantic labels from a
single depth image. With SGC, we further present an efficient 3D sparse
convolutional network, which harnesses a multiscale architecture and a
coarse-to-fine prediction strategy. Evaluations are conducted on the SUNCG
dataset, achieving state-of-the-art performance and fast speed. Code is
available at this https URL

通过空间分组卷积（SGC）来加速 3D 密集预测任务的计算。SGC 是一种与组卷积正交的方法，它作用于空间维度而不是特征通道维度。它将输入体素划分为不同的组，然后在这些分离的组上进行 3D 稀疏卷积。在进行卷积时，只有有效的体素被考虑到，因此可以大大减少计算量并略微降低精度。我们将所提出的操作用于语义场景完成任务，该任务旨在从单个深度图像中预测具有语义标签的完整 3D 体积。基于 SGC，我们进一步提出了一种高效的 3D 稀疏卷积网络，它利用了多尺度架构和粗到精的预测策略。在 SUNCG 数据集上进行了评估，并实现了最先进的性能和快速速度。我们的代码可在 https URL 上获得。