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Point Cloud Registration On 3Dmatch Benchmark

Metrics

Feature Matching Recall

Results

Performance results of various models on this benchmark

Model Name
Feature Matching Recall
Paper TitleRepository
D3Feat-Pred95.8D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features-
3DSmoothNet94.7The Perfect Match: 3D Point Cloud Matching with Smoothed Densities-
PPF-FoldNet71.8PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors-
D3Feat-rand95.3D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features-
IMFNet98.6IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration-
FCGF + RANSAC85Fully Convolutional Geometric Features
MS-SVConv98.43D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning-
DIP94.8Distinctive 3D local deep descriptors-
FPFH + RANSAC44.2Fast Point Feature Histograms (FPFH) for 3D Registration
SpinNet (no code published as of Dec 15 2020)97.6SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration-
3DMatch + RANSAC66.83DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions-
GeDi (no code published as of May 27 2021)97.9Learning general and distinctive 3D local deep descriptors for point cloud registration-
DIP + Point-TnT96.8Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition-
LMVD97.5End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds-
PPFNet62.3PPFNet: Global Context Aware Local Features for Robust 3D Point Matching-
0 of 15 row(s) selected.
Point Cloud Registration On 3Dmatch Benchmark | SOTA | HyperAI