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Autonomous robotic systems and self driving cars rely on accurate perceptionof their surroundings as the safety of the passengers and pedestrians is thetop priority. Semantic segmentation is one the essential components ofenvironmental perception that provides semantic information of the scene.Recently, several methods have been introduced for 3D LiDAR semanticsegmentation. While, they can lead to improved performance, they are eitherafflicted by high computational complexity, therefore are inefficient, or lackfine details of smaller instances. To alleviate this problem, we proposeAF2-S3Net, an end-to-end encoder-decoder CNN network for 3D LiDAR semanticsegmentation. We present a novel multi-branch attentive feature fusion modulein the encoder and a unique adaptive feature selection module with feature mapre-weighting in the decoder. Our AF2-S3Net fuses the voxel based learning andpoint-based learning into a single framework to effectively process the large3D scene. Our experimental results show that the proposed method outperformsthe state-of-the-art approaches on the large-scale SemanticKITTI benchmark,ranking 1st on the competitive public leaderboard competition upon publication.

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