Inferring Point Cloud Quality via Graph Similarity

Objective quality estimation of media content plays a vital role in a wide range of applications. Though numerous metrics exist for 2D images and videos, similar metrics are missing for 3D point clouds with unstructured and non-uniformly distributed points. In this paper, we propose
${\sf GraphSIM}$
—a metric to accurately and quantitatively predict the human perception of point cloud with superimposed geometry and color impairments. Human vision system is more sensitive to the high spatial-frequency components (e.g., contours and edges), and weighs local structural variations more than individual point intensities. Motivated by this fact, we use graph signal gradient as a quality index to evaluate point cloud distortions. Specifically, we first extract geometric
keypoints
by resampling the reference point cloud geometry information to form an object skeleton. Then, we construct
local graphs
centered at these keypoints for both reference and distorted point clouds. Next, we compute three
moments of color gradients
between centered keypoint and all other points in the same local graph for
local significance
similarity feature. Finally, we obtain similarity index by pooling the local graph significance across all color channels and averaging across all graphs. We evaluate
${\sf GraphSIM}$
on two large and independent point cloud assessment datasets that involve a wide range of impairments (e.g., re-sampling, compression, and additive noise).
${\sf GraphSIM}$
provides state-of-the-art performance for all distortions with noticeable gains in predicting the subjective mean opinion score (MOS) in comparison with point-wise distance-based metrics adopted in standardized reference software. Ablation studies further show that
${\sf GraphSIM}$
can be generalized to various scenarios with consistent performance by adjusting its key modules and parameters. Models and associated materials will be made available at
https://njuvision.github.io/GraphSIM
or
http://smt.sjtu.edu.cn/papers/GraphSIM
.