Complexity-Calibrated Reconstruction Discrepancy for No-Reference Point Cloud Quality Assessment
The discrepancy induced by surface reconstruction in a distorted point cloud is jointly influenced by its intrinsic structural complexity and signal degradation.
ProofPaper ↗
Key points
- This observation motivates a referencefree point cloud quality assessment (PCQA) strategy that estimates and compensates for the complexity-induced component.
- Specifically, reconstructing the distorted point cloud produces an input-toreconstruction discrepancy that combines the effects of signal degradation and content complexity.
- Reapplying the reconstruction operator with stronger smoothing produces a discrepancy between successive reconstructions, which serves as a proxy for the complexityinduced component.
- We apply the same strategy to 3D geometry patches and their aligned 2D regions, enabling local comparison of the resulting discrepancies.
Sources (1)
- [1]Complexity-Calibrated Reconstruction Discrepancy for No-Reference Point Cloud Quality AssessmentarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 08:35 AM
The discrepancy induced by surface reconstruction in a distorted point cloud is jointly influenced by its intrinsic structural complexity and signal degradation.
This observation motivates a referencefree point cloud quality assessment (PCQA) strategy that estimates and compensates for the complexity-induced component.
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