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Point Cloud Super Resolution

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 16 papers

TitleStatusHype
SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds—0
R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model—0
EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera—0
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data—0
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation—0
Lightweight super resolution network for point cloud geometry compressionCode1
TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards UpsamplingCode0
ASUR3D: Arbitrary Scale Upsampling and Refinement of 3D Point Clouds using Local Occupancy FieldsCode0
PU-MFA : Point Cloud Up-sampling via Multi-scale Features AttentionCode0
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds—0
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All datasetsSHREC15PU-GAN

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Meta-PUF-measure (%)94.96—Unverified
2AR-GCNF-measure (%)93.1—Unverified
3PU-NETF-measure (%)56.4—Unverified