TP-NoDe

Code for ICCVW-2023 accepted paper TP-NoDe: Topology aware Progressive Noising and Denoising of Point Clouds

Akash Kumbar, Tejas Anvekar, Tulasi Amitha Vikrama, Ramesh Ashok Tabib, Uma Mudenagudi

[Paper]

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.

Installation

• Install the following packages

python==3.8.16
torch==1.13.1
CUDA==11.6
numpy==1.21.2
open3d==0.17.0
einops==0.3.2
scikit-learn==1.0.1
tqdm==4.62.3
h5py==3.6.0
torch-cluster

Install torch-cluster using pip install --verbose --no-cache-dir torch-cluster [https://pytorch-geometric.readthedocs.io/en/1.3.2/notes/installation.html]


Also, for denoising we use score based denoising, install their packages to run this code (please follow score-denoise)

• Compile the evaluation_code for metric calculation (optional)

To calculate the CD, HD and P2F metrics, you need to install the CGAL library (please follow the PU-GAN repo) and virtual environment of PU-GCN (please follow the PU-GCN repo) first. And then you also need to compile the evaluation_code folder.

cd evaluation_code
bash compile.sh

Data Preparation

The code intakes mesh files and random samples it to mentioned number of points in the code. So, no extra pre-processing required.

For benchmarking we use PU-GAN dataset(train set, test mesh)

To run the code as is, prepare a 'data' folder like this:

data  
├───test

Running the code

To run the code:

#The noise hyper-parameters can be changed accordingly (refer to the bash scripts)
python upSampleWithNoise.py --noising global --upsampling_factor 4 --patch_size 64 --seed_k 3 --noise_type Laplacian --save_path data/Final/Global/Laplacian/PS64/

Re-Create Results and ablations

#We have broken down it to two sh files
sh run_all.sh
sh run_allExps.sh

Acknowledgment

Our methodology wholly depends on score-based denoising network and we use their pre-trained weights:

Score-based denoising of Point Clouds.

BibTeX

Please cite our paper if it is helpful to your research:

@inproceedings{kumbar2023tp,
    title={TP-NoDe: Topology-Aware Progressive Noising and Denoising of Point Clouds Towards Upsampling},
    author={Kumbar, Akash and Anvekar, Tejas and Vikrama, Tulasi Amitha and Tabib, Ramesh Ashok and Mudenagudi, Uma},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
    pages={2272--2282},
    year={2023}
}