Autonomous robotic systems and self driving cars rely on accurate perception
of their surroundings as the safety of the passengers and pedestrians is the
top priority. Semantic segmentation is one the essential components of
environmental perception that provides semantic information of the scene.
Recently, several methods have been introduced for 3D LiDAR semantic
segmentation. While, they can lead to improved performance, they are either
afflicted by high computational complexity, therefore are inefficient, or lack
fine details of smaller instances. To alleviate this problem, we propose
AF2-S3Net, an end-to-end encoder-decoder CNN network for 3D LiDAR semantic
segmentation. We present a novel multi-branch attentive feature fusion module
in the encoder and a unique adaptive feature selection module with feature map
re-weighting in the decoder. Our AF2-S3Net fuses the voxel based learning and
point-based learning into a single framework to effectively process the large
3D scene. Our experimental results show that the proposed method outperforms
the state-of-the-art approaches on the large-scale SemanticKITTI benchmark,
ranking 1st on the competitive public leaderboard competition upon publication.

本文介绍了一种基于 AF2-S3Net 的端到端编解码 CNN 网络，用于 3D LiDAR 语义分割，并在大规模 SemanticKITTI 基准测试上展示了其优越性能。

(AF) 2-S3Net: 基于自适应特征选择的注意力特征融合稀疏语义分割网络

(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection  for Sparse Semantic Segmentation Network

Creating high definition maps that contain precise information of static
elements of the scene is of utmost importance for enabling self driving cars to
drive safely. In this paper, we tackle the problem of drivable road boundary
extraction from LiDAR and camera imagery. Towards this goal, we design a
structured model where a fully convolutional network obtains deep features
encoding the location and direction of road boundaries and then, a
convolutional recurrent network outputs a polyline representation for each one
of them. Importantly, our method is fully automatic and does not require a user
in the loop. We showcase the effectiveness of our method on a large North
American city where we obtain perfect topology of road boundaries 99.3% of the
time at a high precision and recall.

本文介绍了一种全自动的深度学习方法，用于从 LiDAR 和相机图像中提取驾驶道路边界的拓扑结构，通过结构的全卷积网络和卷积循环网络，本方法可获得高精度和召回率的驾驶道路拓扑结构。