Multi-Resolution Convolution for 3D Semantic Segmentation

Effective capture of multi-scale features is crucial for improving performance in 3D point cloud semantic segmentation tasks. This paper introduces a novel framework that enhances the extraction of semantic information from complex objects in 3D point clouds using multi-resolution techniques. By utilizing varying voxel resolutions and convolutional kernel sizes, we integrate high-resolution voxels to capture fine details and low-resolution voxels to extract global features, achieving robust feature fusion. Experimental results demonstrate the effectiveness of our proposed network validated on the ScanNet v2 dataset, particularly excelling in the semantic segmentation task for small objects and complex scenes. This study highlights the significance of multi-resolution strategies in 3D scene understanding, providing new insights for future research in the field.