This repository contains codes of ICCV2021 paper: Edge-Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA). The proposed PU-EVA decouples the upsampling scales with network architecture, making the upsampling rate flexible in one-time end-to-end training.
High-quality point clouds have practical significance for point-based rendering, semantic understanding, and surface reconstruction. Upsampling sparse, noisy and non-uniform point clouds for a denser and more regular approximation of target objects is a desirable but challenging task. Most existing methods duplicate point features for upsampling, constraining the upsampling scales at a fixed rate. In this work, the arbitrary point clouds upsampling rates are achieved via edge-vector based affine combinations, and a novel design of Edge-Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed. The edge-vector based approximation encodes neighboring connectivity via affine combinations based on edge vectors, and restricts the approximation error within a second-order term of Taylor's Expansion. Moreover, the EVA upsampling decouples the upsampling scales with network architecture, achieving the arbitrary upsampling rates in one-time training. Qualitative and quantitative evaluations demonstrate that the proposed PU-EVA outperforms the state-of-the-arts in terms of proximity-to-surface, distribution uniformity, and geometric details preservation.
- Run the training script:
python main.py- Run the evaluation script after training finished:
python evalutate.py
@InProceedings{Luo_2021_ICCV, author = {Luo, Luqing and Tang, Lulu and Zhou, Wanyi and Wang, Shizheng and Yang, Zhi-Xin}, title = {PU-EVA: An Edge-Vector Based Approximation Solution for Flexible-Scale Point Cloud Upsampling}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {16208-16217} }
MIT License
This code is heavily borrowed from PointNet, DGCNN and PU-GAN.