{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tp-node-topology-aware-progressive-noising","title":"TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards Upsampling","arxiv_id":null,"date":"2023-10-02","proceeding":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops 2023 10","authors":["Akash Kumbar","Tejas Anvekar","Tulasi Amitha Vikrama","Ramesh Ashok Tabib","Uma Mudenagudi"],"abstract":"In this paper, we propose TP-NoDe, a novel Topology-aware Progressive Noising and Denoising technique for 3D point cloud upsampling. TP-NoDe revisits the traditional method of upsampling of the point cloud by introducing a novel perspective of adding local topological noise by incorporating a novel algorithm Density-Aware k nearest neighbour (DA-kNN) followed by denoising to map noisy perturbations to the topology of the point cloud. Unlike previous methods, we progressively upsample the point cloud, starting at a 2 x upsampling ratio and advancing to a desired ratio. TP-NoDe generates intermediate upsampling resolutions for free, obviating the need to train different models for varying upsampling ratios. TP-NoDe mitigates the need for task-specific training of upsampling networks for a specific upsampling ratio by reusing a point cloud denoising framework. We demonstrate the supremacy of our method TP-NoDe on the PU-GAN dataset and compare it with state-of-the-art upsampling methods. The code is publicly available at https://github.com/Akash-Kumbar/TPNoDe.","url_abs":"https://openaccess.thecvf.com/content/ICCV2023W/WiCV/html/Kumbar_TP-NoDe_Topology-Aware_Progressive_Noising_and_Denoising_of_Point_Clouds_Towards_ICCVW_2023_paper.html","url_pdf":"https://openaccess.thecvf.com/content/ICCV2023W/WiCV/papers/Kumbar_TP-NoDe_Topology-Aware_Progressive_Noising_and_Denoising_of_Point_Clouds_Towards_ICCVW_2023_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tp-node-topology-aware-progressive-noising","repo_url":"https://github.com/Akash-Kumbar/TP-NoDe","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"point-cloud-super-resolution","task_name":"Point Cloud Super Resolution"},{"task_slug":"point-cloud-upsampling","task_name":"point cloud upsampling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}