SVFNeXt: Sparse Voxel Fusion for LiDAR-Based 3D Object Detection

Voxel-based 3D object detection methods have gained more popularity in autonomous driving. However, due to the sparse nature of LiDAR point clouds, voxels from conventional cubic partition lead to incomplete representation of objects in farther range. This poses significant challenges to 3D object perception. In this paper, we propose a novel 3D object detector dubbed SVFNeXt, a Sparse Voxel Fusion Network that performs cross-representation (X) feature learning. It is because cylindrical voxel representation considers the rotational or radial scanning of LiDAR that we can better explore the inherent 3D geometric structure of point clouds. To further enchance cubic voxel features, we innovatively integrates the features of cylindrical voxels into cubic voxels, incorporating both local and global features. We particularly attend to informative voxels by two additional losses, striking a good speed-accuracy tradeoff. Extensive experiments on the WOD and KITTI datasets demonstrate consistent improvements over baselines. Our SVFNeXt achieves competitive results compared to state-of-the-art methods, especially for small objects(e.g., cyclist, pedestrian).