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Point Cloud Registration On Kitti Trained On

评估指标

Success Rate

评测结果

各个模型在此基准测试上的表现结果

Paper Title
GeDi98.92Learning general and distinctive 3D local deep descriptors for point cloud registration
FCGF+SC2-PCR97.66SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration
FCGF+PointDSC96.76PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
Exhaustive Grid Search94.95Addressing the generalization of 3D registration methods with a featureless baseline and an unbiased benchmark
FPFH+PointDSC94.05PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
DIP93.51Distinctive 3D local deep descriptors
Greedy Grid Search90.27Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration
YOHO-C82.16You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
YOHO-O81.44You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
SpinNet81.44SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
GeoTransformer67.93GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
Predator41.20PREDATOR: Registration of 3D Point Clouds with Low Overlap
D3Feat-pred36.76D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
FCGF24.19Fully Convolutional Geometric Features
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Point Cloud Registration On Kitti Trained On | SOTA | HyperAI超神经