Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentation
This work addresses the problem of accurate semantic segmentation in 3D point clouds for applications like autonomous driving and robotics, representing an incremental improvement over existing methods.
The paper tackles point cloud semantic segmentation by proposing a network that aggregates local and global multi-scale information, achieving state-of-the-art performance on benchmark datasets.
In this paper, we present a comprehensive point cloud semantic segmentation network that aggregates both local and global multi-scale information. First, we propose an Angle Correlation Point Convolution (ACPConv) module to effectively learn the local shapes of points. Second, based upon ACPConv, we introduce a local multi-scale split (MSS) block that hierarchically connects features within one single block and gradually enlarges the receptive field which is beneficial for exploiting the local context. Third, inspired by HRNet which has excellent performance on 2D image vision tasks, we build an HRNet customized for point cloud to learn global multi-scale context. Lastly, we introduce a point-wise attention fusion approach that fuses multi-resolution predictions and further improves point cloud semantic segmentation performance. Our experimental results and ablations on several benchmark datasets show that our proposed method is effective and able to achieve state-of-the-art performances compared to existing methods.