In this paper, we first analyze the accuracy of 3D object reconstruction using point cloud filtering applied on data from a RGB-D sensor. Point cloud filtering algorithms carry out upsampling for defective point cloud. Various methods of point cloud filtering are tested and compared with respect to the reconstruction accuracy using real data. In order to improve the accuracy of 3D object reconstruction, an efficient method of point cloud filtering is designed. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed point cloud filtering algorithm.
The 3D object reconstruction is a popular task in the field of medicine, agriculture, architecture,
games, and film industry [
RGB-D low-cost sensors such as the Kinect can provide a high-resolution RGB color image
with a depth map of the environment [
The noise and holes can greatly affect to the accurate of 3D reconstruction [
In this paper, we are interested in the design of a filtering algorithm of a poin cloud to improve
the quality of the 3D reconstruction. In recent years, a large number of methods contributing to
3D point cloud filtering have been proposed: Normal-based Bilateral Filter [
In a common approach of noise reduction it is supposed that the raw point cloud contains
the ground truth, distorted by artificial noise such as additive [
General denoising methods are not designed to clean coarse noise contained in the input point cloud. Therefore, our main goal is to evaluate denoising methods in terms of reconstruction accuracy which depends on the quality of the input point cloud.
The paper is organized as follows. Section 2 discusses related denoising point cloud methods. Computer simulation results are provided in Section 3. Finally, Section 4 summarizes our conclusions.
This section contains information about point cloud denoising filters.
The paper [
SOR uses point neighborhood statistics to filter outlier data [
ROR removes outliers if the number of neighbors in a certain search radius is smaller than a
given K [
VG filtering method first defines a 3D voxel grid (3D boxes in 3D space) on a point cloud. Then, in each voxel, a point is chosen to approximate all the points that lie on that voxel. Normally, the centroid of these points or the center of this voxel is used as the approximation. The former is slower than the later, while its representative of underlying surface is more accurate. The VG method usually leads to geometric information loss.
3DBF filter denoises a point with respect to its neighbors by considering not only the distance
from the neighbors to the point but also the distance along a normal direction [
Let us first consider a point cloud M with known normals nv at each vertex position v. Let N (v) be the 1-ring neighborhood of vertex v (i.e. the set of vertices sharing an edge with v). Then, the filtered position of v is
v + δv · nv, where δv =
Pp∈N(v) wd(kp − vk)wn(| hnv, p − vi |) h nv, p − vi
Pp∈N(v) wd(kp − vk)wn(| hnv, p − vi |) and wd and wn are two decreasing functions. Here vertex v is shifted along its normal toward a weighted average of points that are both close to v in the ambient space and close to the plane passing through v with normal nv.
In this section, we evaluate the performance of the tested denoising methods in terms of reconstruction accuracy which depends on the quality of the input point cloud.
We compare the following point cloud denoising algorithms in terms of the accuracy of 3D
object reconstruction and speed: Statistical Outlier Removal filter (SOR) [
In our experiments we use the point clouds of a lion and an apollo from dataset [
We construct couples of point clouds for each model using the following steps: (i) Registration RGB and depth data (Fig. 1). (ii) Making point clouds (Fig. 2). (iii) Computing point cloud statistics, such as points count, minimum, maximum and median distance between points in point cloud (Table. 1). Point clouds statistics is required for further calculation of optimum parameters of 3D filters. (iv) Metric calculation algorithms between couples of frames of point clouds. We calculate transorfmation matrix with standart ICP algorithm and euclidian fitness score (ICP error). Since filtered point cloud can contain different points count of rather initial cloud, we also calculate the hausdorf distance between initial and filtered clouds for estimation of the quality of 3D filtration.
Result of calculation and visualization of the hausdorf metric between two frames of chair model shown in Fig. 3.
The metrics between two frames of each model without filtering are presented in Table 2.
The corresponding ICP error and hausdorf distance calculated for chair model with SOR, ROR, VG, 3DBF point cloud denoising algorithms are shown in Table 3. The ROR filter yields the best result in terms of ICP error and hausdorf distance evaluation among all point cloud denoising algorithms. Fig. 4 shows the point clouds of a chair after denoising ROR and SOR filtering.
In this paper, we compared various point cloud algorithms in terms of accuracy of 3D object reconstruction using real data from a RGB-D sensor. The experiment has shown that the ROR filter yields the best result in terms of ICP error and hausdorf distance evaluation among all point cloud denoising algorithms.