Point Cloud Super Resolution
10 papers with code • 2 benchmarks • 1 datasets
Point cloud super-resolution is a fundamental problem for 3D reconstruction and 3D data understanding. It takes a low-resolution (LR) point cloud as input and generates a high-resolution (HR) point cloud with rich details
SHREC
Latest papers
Lightweight super resolution network for point cloud geometry compression
lidq92/lsrn-pcgc
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This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network.
TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards Upsampling
Akash-Kumbar/TP-NoDe
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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops 2023
TP-NoDe mitigates the need for task-specific training of upsampling networks for a specific upsampling ratio by reusing a point cloud denoising framework.
ASUR3D: Arbitrary Scale Upsampling and Refinement of 3D Point Clouds using Local Occupancy Fields
Our proposed implicit occupancy representation enables efficient point classification, effectively discerning points belonging to the surface from non-surface points.
PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention
rhtm02/PU-MFA
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The performance of PU-MFA was compared with other state-of-the-art methods through various experiments using the PU-GAN dataset, which is a synthetic point cloud dataset, and the KITTI dataset, which is the real-scanned point cloud dataset.
Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud
Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
Matrix $\mathbf T$ approximates the augmented Jacobian matrix of a local parameterization and builds a one-to-one correspondence between the 2D parametric domain and the 3D tangent plane so that we can lift the adaptively distributed 2D samples (which are also learned from data) to 3D space.
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
guochengqian/PU-GCN
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CVPR 2021
We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN.
PU-GAN: a Point Cloud Upsampling Adversarial Network
Point clouds acquired from range scans are often sparse, noisy, and non-uniform.
Patch-based Progressive 3D Point Set Upsampling
guochengqian/PU-GCN
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CVPR 2019
We present a detail-driven deep neural network for point set upsampling.
PU-Net: Point Cloud Upsampling Network
Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.
