HyperAI超神经
首页
资讯
最新论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
中文
HyperAI超神经
Toggle sidebar
全站搜索…
⌘
K
首页
SOTA
Point Cloud Registration
Point Cloud Registration On 3Dmatch At Least 1
Point Cloud Registration On 3Dmatch At Least 1
评估指标
RE (all)
Recall (0.3m, 15 degrees)
TE (all)
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
RE (all)
Recall (0.3m, 15 degrees)
TE (all)
Paper Title
Repository
PCAM-Sparse (All post-processing)
8.9
92.4
0.23
PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
GeoTransformer
-
95
-
Geometric Transformer for Fast and Robust Point Cloud Registration
RANSAC-2M
-
66.1
-
Fast Point Feature Histograms (FPFH) for 3D Registration
DCP
-
3.22
-
Deep Closest Point: Learning Representations for Point Cloud Registration
ICP (P2Plane)
-
6.59
-
Open3D: A Modern Library for 3D Data Processing
Super4PCS
-
21.6
-
Super 4PCS Fast Global Pointcloud Registration via Smart Indexing
Go-ICP
-
22.9
-
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
-
PointNetLK
-
1.61
-
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
FGR
-
42.7
-
Fast Global Registration
Exhaustive Grid Search
-
84.11
-
Addressing the generalization of 3D registration methods with a featureless baseline and an unbiased benchmark
ICP (P2Point)
-
6.04
-
Open3D: A Modern Library for 3D Data Processing
PCAM-Soft (All post-processing)
9.8
91.3
0.24
PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
DGR (RE (all), TE(all) are reported in PCAM)
9.5
91.3
0.25
Deep Global Registration
NgeNet
4.932
95.0
0.155
Leveraging Inlier Correspondences Proportion for Point Cloud Registration
0 of 14 row(s) selected.
Previous
Next