一、论文解读
[1] This paper addresses the problem of recognizing free form 3D objects in point clouds.
[2] Compared to traditional approaches based on point descriptors, which depend on local information around points, we propose a novel method that creates a global model description based on oriented point pair features and matches that model locally using a fast voting scheme.
[3] The global model description consists of all model point pair features and represents a mapping from the point pair feature space to the model, where similar features on the model are grouped together.
[4] Such representation allows using much sparser object and scene point clouds, resulting in very fast performance.
[5] Recognition is done locally using an efficient voting scheme on a reduced two-dimensional search space.
[6] We demonstrate the efficiency of our approach and show its high recognition performance in the case of noise, clutter and partial occlusion.
[7] Compared to state of the art approaches we achieve better recognition rates, and demonstrate that with a slight or even no sacrifice of the recognition performance our method is much faster then the current state of the art approaches.
1. Model Globally
scene
和 model
,scene 是我们测得的真实场景(点云),model 是物体的真实模型(点云)。Both the scene and the model are represented as a finite set of oriented points, where a normal is associated with each point.
points in the scene points in the model
The model is represented by a set of point pair features with similar feature vectors being grouped together.
The global model description is a mapping from the sampled point pair feature space to the model.
2. Match Locally
二、 OpenCV 实现
三、Matlab 实现
mex mex/computePPFmex.cpp
mex mex/computePPFmex.cpp
mex mex/MurmurHash3.cpp
四、PCL 实现
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