You Should Learn to Stop Denoising on Point Clouds in Advance

Abstract

Point clouds have become the preferred data format for a variety of tasks in 3D vision and graphics. However, raw point clouds often contain significant noise. This paper introduces the Adaptive Stop Denoising Network (ASDN), a novel approach aimed at restoring high-quality point clouds from noisy data. Our method is built upon a pivotal observation: during the denoising phase, high-noise points draw more focus from the network, which may suppress the points that have already been effectively denoised. This observation has led us to develop an adaptive strategy that ceases denoising already cleaned points to prevent over-denoising, while continuing to refine points that remain noisy. We employ a U-Net architecture complemented by an adaptive classifier, which utilizes a recoverability factor to assess the completion of denoising and make dynamic decisions about when to halt the process. Our method not only demonstrates superior noise removal efficiency but also preserves geometric details more effectively, reducing over- or under-denoising artifacts. Extensive experiments and evaluations demonstrate that our method outperforms the state-of-the-art both qualitatively and quantitatively.