Panoptic segmentation brings together two separate tasks: instance and
semantic segmentation. Although they are related, unifying them faces an
apparent paradox: how to learn simultaneously instance-specific and
category-specific (i.e. instance-agnostic) representations jointly. Hence,
state-of-the-art panoptic segmentation methods use complex models with a
distinct stream for each task. In contrast, we propose Hierarchical Lov\'asz
Embeddings, per pixel feature vectors that simultaneously encode instance- and
category-level discriminative information. We use a hierarchical Lov\'asz hinge
loss to learn a low-dimensional embedding space structured into a unified
semantic and instance hierarchy without requiring separate network branches or
object proposals. Besides modeling instances precisely in a proposal-free
manner, our Hierarchical Lov\'asz Embeddings generalize to categories by using
a simple Nearest-Class-Mean classifier, including for non-instance "stuff"
classes where instance segmentation methods are not applicable. Our simple
model achieves state-of-the-art results compared to existing proposal-free
panoptic segmentation methods on Cityscapes, COCO, and Mapillary Vistas.
Furthermore, our model demonstrates temporal stability between video frames.

Hierarchical Lovasz Embeddings 是一种新的、简单的、基于空间嵌入的、提议自由的、可同时学习实例和类别级信息的方法，可以精确地建模实例，并使用简单的最近类均值分类器来推广到类别。此方法在 Cityscapes、COCO 和 Mapillary Vistas 上达到了最先进的成果，并展示了视频帧之间的时态稳定性。

基于分级 Lovász 嵌入的无需提议的全景分割

Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation

Panoptic segmentation has recently unified semantic and instance
segmentation, previously addressed separately, thus taking a step further
towards creating more comprehensive and efficient perception systems. In this
paper, we present Panoster, a novel proposal-free panoptic segmentation method
for LiDAR point clouds. Unlike previous approaches relying on several steps to
group pixels or points into objects, Panoster proposes a simplified framework
incorporating a learning-based clustering solution to identify instances. At
inference time, this acts as a class-agnostic segmentation, allowing Panoster
to be fast, while outperforming prior methods in terms of accuracy. Without any
post-processing, Panoster reached state-of-the-art results among published
approaches on the challenging SemanticKITTI benchmark, and further increased
its lead by exploiting heuristic techniques. Additionally, we showcase how our
method can be flexibly and effectively applied on diverse existing semantic
architectures to deliver panoptic predictions.

本文提出了一种名为 Panoster 的新型无需分类器的全景分割方法，通过学习聚类解决方案来生成类不可知分割，不仅快速而且能够在 SemanticKITTI 基准测试中超过现有方法，并展示了如何在现有的语义架构中灵活地有效地应用我们的方法来提供全景预测。