Abstract: 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.