Registration / 3.Efficient Sparse ICP
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Efficient Sparse ICP

稀疏ICP方法采用稀疏诱导准则对该问题进行求解，显著提高了配准过程对大量噪声和离群值的恢复能力，但引入了显著的性能退化。

本文首先分析了性能下降的原因，提出了一种结合模拟退火搜索和标准稀疏ICP的混合优化系统，以更有效地解决底层优化问题。

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3D Reconstruction 3D scene Reconstruction 4PCS C++ CUDA CV-Source Deep Learning Despriptors Efficient Sparse ICP FastICP GOICP Generalized-ICP Generic Programming Hash Map ICP LM Memory Management NDT ObjectDetection OpenGL PCL Path Planning Performance PoseEstimation RANSAC Reconstruction Registration SAC-IA SFM SSE STL SVD Segmentation Sparse ICP Super4PCS books cs231n git glfw hexo keypoints mv-LM-ICP std surface Reconstruction tools 图像 编程知识与VS错误

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3D Reconstruction 3
3D scene Reconstruction 1
4PCS 1
C++ 13
CUDA 16
CV-Source 4
Deep Learning 3
Despriptors 1
Efficient Sparse ICP 1
FastICP 1
GOICP 1
Generalized-ICP 2
Generic Programming 1
Hash Map 1
ICP 7
LM 1
Memory Management 1
NDT 2
ObjectDetection 5
OpenGL 3
PCL 8
Path Planning 1
Performance 1
PoseEstimation 7
RANSAC 1
Reconstruction 5
Registration 11
SAC-IA 1
SFM 1
SSE 1
STL 2
SVD 1
Segmentation 1
Sparse ICP 1
Super4PCS 1
books 1
cs231n 1
git 2
glfw 2
hexo 2
keypoints 1
mv-LM-ICP 1
std 1
surface Reconstruction 1
tools 1
图像 2
编程知识与VS错误 1

最新文章

归档

标签

3D Reconstruction 3
3D scene Reconstruction 1
4PCS 1
C++ 13
CUDA 16
CV-Source 4
Deep Learning 3
Despriptors 1
Efficient Sparse ICP 1
FastICP 1
GOICP 1
Generalized-ICP 2
Generic Programming 1
Hash Map 1
ICP 7
LM 1
Memory Management 1
NDT 2
ObjectDetection 5
OpenGL 3
PCL 8
Path Planning 1
Performance 1
PoseEstimation 7
RANSAC 1
Reconstruction 5
Registration 11
SAC-IA 1
SFM 1
SSE 1
STL 2
SVD 1
Segmentation 1
Sparse ICP 1
Super4PCS 1
books 1
cs231n 1
git 2
glfw 2
hexo 2
keypoints 1
mv-LM-ICP 1
std 1
surface Reconstruction 1
tools 1
图像 2
编程知识与VS错误 1
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