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Given the prominence of current 3D sensors, a fine-grained analysis on thebasic point cloud data is worthy of further investigation. Particularly, realpoint cloud scenes can intuitively capture complex surroundings in the realworld, but due to 3D data's raw nature, it is very challenging for machineperception. In this work, we concentrate on the essential visual task, semanticsegmentation, for large-scale point cloud data collected in reality. On the onehand, to reduce the ambiguity in nearby points, we augment their local contextby fully utilizing both geometric and semantic features in a bilateralstructure. On the other hand, we comprehensively interpret the distinctness ofthe points from multiple resolutions and represent the feature map following anadaptive fusion method at point-level for accurate semantic segmentation.Further, we provide specific ablation studies and intuitive visualizations tovalidate our key modules. By comparing with state-of-the-art networks on threedifferent benchmarks, we demonstrate the effectiveness of our network.

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion