Panoptic segmentation presents a new challenge in exploiting the merits of
both detection and segmentation, with the aim of unifying instance segmentation
and semantic segmentation in a single framework. However, an efficient solution
for panoptic segmentation in the emerging domain of LiDAR point cloud is still
an open research problem and is very much under-explored. In this paper, we
present a fast and robust LiDAR point cloud panoptic segmentation framework,
referred to as Panoptic-PolarNet. We learn both semantic segmentation and
class-agnostic instance clustering in a single inference network using a polar
Bird's Eye View (BEV) representation, enabling us to circumvent the issue of
occlusion among instances in urban street scenes. To improve our network's
learnability, we also propose an adapted instance augmentation technique and a
novel adversarial point cloud pruning method. Our experiments show that
Panoptic-PolarNet outperforms the baseline methods on SemanticKITTI and
nuScenes datasets with an almost real-time inference speed. Panoptic-PolarNet
achieved 54.1% PQ in the public SemanticKITTI panoptic segmentation leaderboard
and leading performance for the validation set of nuScenes.

提出了一种名为 Panoptic-PolarNet 的快速而强健的 LiDAR 点云全景分割框架，使用极坐标鸟瞰图表示进行语义分割和类无关实例聚类，以解决城市街景中实例的遮挡问题，实验结果表明 Panoptic-PolarNet 在 benchmark 数据集上表现优异。

Panoptic-PolarNet：无需提议的 LiDAR 点云全景分割

Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation

Deep Convolutional Neural Networks (DCNNs) have recently shown outstanding
performance in semantic image segmentation. However, state-of-the-art
DCNN-based semantic segmentation methods usually suffer from high computational
complexity due to the use of complex network architectures. This greatly limits
their applications in the real-world scenarios that require real-time
processing. In this paper, we propose a real-time high-performance DCNN-based
method for robust semantic segmentation of urban street scenes, which achieves
a good trade-off between accuracy and speed. Specifically, a Lightweight
Baseline Network with Atrous convolution and Attention (LBN-AA) is firstly used
as our baseline network to efficiently obtain dense feature maps. Then, the
Distinctive Atrous Spatial Pyramid Pooling (DASPP), which exploits the
different sizes of pooling operations to encode the rich and distinctive
semantic information, is developed to detect objects at multiple scales.
Meanwhile, a Spatial detail-Preserving Network (SPN) with shallow convolutional
layers is designed to generate high-resolution feature maps preserving the
detailed spatial information. Finally, a simple but practical Feature Fusion
Network (FFN) is used to effectively combine both shallow and deep features
from the semantic branch (DASPP) and the spatial branch (SPN), respectively.
Extensive experimental results show that the proposed method respectively
achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU)
with the inference speed of 51.0 fps and 39.3 fps on the challenging Cityscapes
and CamVid test datasets (by only using a single NVIDIA TITAN X card). This
demonstrates that the proposed method offers excellent performance at the
real-time speed for semantic segmentation of urban street scenes.

本文提出了一种用于城市街景的语义分割的高性能深卷积神经网络方法，它实现了精度和速度之间的良好权衡，并展示了在实时速度下使用单个 NVIDIA TITAN X 卡片在具有挑战性的 Cityscapes 和 CamVid 测试数据集上分别实现了 73.6％和 68.0％的 mIoU 精度。

城市街景的实时高性能语义图像分割

Real-Time High-Performance Semantic Image Segmentation of Urban Street  Scenes

Tracking in urban street scenes plays a central role in autonomous systems
such as self-driving cars. Most of the current vision-based tracking methods
perform tracking in the image domain. Other approaches, eg based on LIDAR and
radar, track purely in 3D. While some vision-based tracking methods invoke 3D
information in parts of their pipeline, and some 3D-based methods utilize
image-based information in components of their approach, we propose to use
image- and world-space information jointly throughout our method. We present
our tracking pipeline as a 3D extension of image-based tracking. From enhancing
the detections with 3D measurements to the reported positions of every tracked
object, we use world-space 3D information at every stage of processing. We
accomplish this by our novel coupled 2D-3D Kalman filter, combined with a
conceptually clean and extendable hypothesize-and-select framework. Our
approach matches the current state-of-the-art on the official KITTI benchmark,
which performs evaluation in the 2D image domain only. Further experiments show
significant improvements in 3D localization precision by enabling our coupled
2D-3D tracking.

在自动驾驶等自主系统中，跟踪在城市街道场景中起着核心作用。本文提出了一种基于图像和世界空间信息联合的三维扩展跟踪方法，通过我们的新型耦合 2D-3D Kalman 滤波器和一个干净且可扩展的假设和选择框架，实现了从增强检测到跟踪对象到每个跟踪对象所报告的位置的全过程中使用世界空间 3D 信息。我们的方法在官方的 KITTI 基准测试上与当前最先进的技术相匹配，在启用耦合 2D-3D 跟踪后，在三维定位精度方面显示出显著的改进。