Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Simple and Efficient Registration of 3D Point Cloud and Image Data for
  Indoor Mobile Mapping System   Registration of 3D LiDAR point clouds with optical images is critical in the
combination of multi-source data. Geometric misalignment originally exists in
the pose data between LiDAR point clouds and optical images. To improve the
accuracy of the initial pose and the applicability of the integration of 3D
points and image data, we develop a simple but efficient registration method.
We firstly extract point features from LiDAR point clouds and images: point
features is extracted from single-frame LiDAR and point features from images
using classical Canny method. Cost map is subsequently built based on Canny
image edge detection. The optimization direction is guided by the cost map
where low cost represents the the desired direction, and loss function is also
considered to improve the robustness of the the purposed method. Experiments
show pleasant results.

27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ