Recovering dense and uniformly distributed point clouds from sparse or noisy data remains a significant challenge. Recently, great progress has been made on these tasks, but usually at the cost of increasingly intricate modules or complicated network architectures, leading to long inference time and huge resource consumption. Instead, we embrace simplicity and present a simple yet efficient method for jointly upsampling and cleaning point clouds. Our method leverages an off-the-shelf octree-based 3D U-Net (OUNet) with minor modifications, enabling both upsampling and cleaning within a single network. Our network directly processes each input point cloud as a whole instead of processing point cloud patches as in previous works, which significantly eases the implementation and brings at least 47 times faster inferencing. Extensive experiments demonstrate that our method achieves state-of-the-art performance with huge efficiency advantages on a series of benchmarks. We expect our method to serve as a simple baseline and inspire researchers to rethink method designs for point cloud upsampling and cleaning. Our code and trained models are available at https://github.com/octree-nn/upsample-clean.