From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training
Journal:
arXiv
Published Date:
Jan 23, 2025
Abstract
We present a novel quality assessment method which can predict the perceptual
quality of point clouds from new scenes without available annotations by
leveraging the rich prior knowledge in images, called the Distribution-Weighted
Image-Transferred Point Cloud Quality Assessment (DWIT-PCQA). Recognizing the
human visual system (HVS) as the decision-maker in quality assessment
regardless of media types, we can emulate the evaluation criteria for human
perception via neural networks and further transfer the capability of quality
prediction from images to point clouds by leveraging the prior knowledge in the
images. Specifically, domain adaptation (DA) can be leveraged to bridge the
images and point clouds by aligning feature distributions of the two media in
the same feature space. However, the different manifestations of distortions in
images and point clouds make feature alignment a difficult task. To reduce the
alignment difficulty and consider the different distortion distribution during
alignment, we have derived formulas to decompose the optimization objective of
the conventional DA into two suboptimization functions with distortion as a
transition. Specifically, through network implementation, we propose the
distortion-guided biased feature alignment which integrates existing/estimated
distortion distribution into the adversarial DA framework, emphasizing common
distortion patterns during feature alignment. Besides, we propose the
quality-aware feature disentanglement to mitigate the destruction of the
mapping from features to quality during alignment with biased distortions.
Experimental results demonstrate that our proposed method exhibits reliable
performance compared to general blind PCQA methods without needing point cloud
annotations.
