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DI-PCQA: Domain-Invariant Learning Framework for No-Reference Point Cloud Quality Assessment
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DOI：10.1109/tbc.2026.3689330.png)
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Abstract
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Recent advances in no-reference point cloud quality assessment (NR-PCQA) have been largely driven by deep learning techniques. To further enhance prediction accuracy, existing methods often rely on multi-modal feature fusion. However, the introduction of multimodality ignores the consideration of the model’s generalization ability. Moreover, it often introduces additional computational overhead inference. To address these challenges, we propose a novel NR-PCQA method based on domain-invariant learning framework (DI-PCQA). Specifically, the input point cloud is first projected to obtain a spatial-domain visual representation. To construct different representations with the same origin and semantics, this spatial information is further transformed into a frequency domain representation. Both spatial and frequency features are then fed into a weight-shared encoder to extract feature. Subsequently, an adversarial domain classification task is introduced. We attach the gradient reversal layer to the weight-shared encoder, enabling the encoder to generate features that can confuse the domain classifier. This adversarial training encourages the weight-shared encoder to learn domain-invariant semantic features rather than overfitting to a single data distribution. And the spatial features are simultaneously regressed to quality scores. Finally, DI-PCQA employs a decoupled training-testing strategy. It leverages both spatial and frequency domain inputs to learn domain-invariant representations during training, but performs efficient quality prediction using only spatial information at test time. We conduct extensive tests on public databases and achieve superior performance. Further evaluations confirm its generalization ability and fast inference speed.
Keywords:
Point cloud quality assessment
deep neural network
domain-invariant learning strategy
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