DAPU: Distribution-aware Patch Upsampling for Point Cloud Based 3D Object Detection

The sparsity and quality of point clouds significantly constrain the development of LiDAR-based 3D object de tectors. Previous approaches supplemented point clouds through depth completion or upsampling. However, the former suffers from inconsistencies caused by differences in multimodal data, resulting in uneven point cloud quality. Meanwhile, previous upsampling methods convert point clouds into range images which results in a loss of point accuracy. In this paper, we present DAPU, a novel real-time point cloud upsampling method designed to address these challenges. This method consists of three key components: (1) the Ground Points Recognizer (GPR), which analyzes the height difference distribution between coplanar and non-coplanar points within patches to identify ground points, establishes a sparse-to-dense index matrix for fast large-scale point cloud queries; (2) the Distribution-Aware Patch KNN (DAPKNN), which dynamically adjusts the sampling radius threshold based on distribution to reduce computation and ensure sufficient neighbor sampling for distant points; (3) the Neighbors Upsampling, which linearly upsamples between each pair of neighbors to preserve all point features. KITTI experiments show gains of up to +1.2% AP 3D and +1.4% AP BEV. Additional evaluations on mini-nuScenes and Waymo further demonstrate consistent improvements across Vehicle, Pedestrian, and Cyclist detection, confirming DAPU's robustness under diverse LiDAR settings and real-time suitability.