TP-NoDe: Topology-Aware Progressive Noising and Denoising of Point Clouds Towards Upsampling

Akash Kumbar, Tejas Anvekar, Tulasi Amitha Vikrama, Ramesh Ashok Tabib, Uma Mudenagudi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 2272-2282

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.