No-Reference Point Cloud Quality Assessment via Contextual Point-Wise Deep Learning Network
Communications in computer and information science(2023)
摘要
For the processing of point clouds, an accurate assessment of the quality is essential. However, point cloud quality assessment has proven to be a difficult issue, especially when the pristine point clouds are unavailable. Most existing no-reference point cloud quality assessment methods adopt projection-based routes, which inevitably suffer from occlusion and misalignment, resulting in loss of information. Alternatively, this paper proposes a novel no-reference point cloud quality assessment method via a contextual point-wise deep learning network (CPW-Net). Compared with projection-based methods, it reduces information loss by learning features directly from point coordinates and attributes. In particular, CPW-Net utilizes an Offset Attention Feature Encoder (OAFE) module to extract local and contextual features. Experiment results demonstrate that the proposed method overwhelms most publicly available no-reference metrics on SJTU dataset and gains compatible performance in comparison with most full-reference methods.
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关键词
quality assessment,cloud,deep learning,no-reference,point-wise
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