← 研踪 PaperTrailCVF · CVPR 2026:cvf:95d760ff8826b997135979e6cc1f3e66 · 记录 #1
R3-PCQA: Ray-Reprojection-Reinforcement for No-Reference 3D Point Cloud Quality Assessment
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:05
摘要
Prevailing no-reference 3D point cloud quality assessment methods predominantly treat 2D projections and 3D point clouds as independent modalities and rely on simplistic feature fusion, thereby neglecting fundamental mechanisms underlying human 3D perception. To address this limitation, we introduce R3-PCQA (Ray-Reprojection-Reinforcement 3D Point Cloud Quality Assessor), a novel and principled framework that explicitly encodes perceptual priors into the assessment pipeline: A geometric-aware ray-based reprojection pipeline simulates viewpoint-dependent observation of 3D structure. A reinforcement-learning-based quality-salient subcloud selector adaptively attends to perceptually informative regions. The global view attention module aggregates local quality responses across viewpoints, forming a unified representation that facilitates reliable cross-view inference. Extensive experiments demonstrate that R3-PCQA achieves state-of-the-art performance on SJTU-PCQA, WPC, and WPC2.0.
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记录 #1 · 2026 · 最新观测版本
R3-PCQA: Ray-Reprojection-Reinforcement for No-Reference 3D Point Cloud Quality Assessment
Prevailing no-reference 3D point cloud quality assessment methods predominantly treat 2D projections and 3D point clouds as independent modalities and rely on simplistic feature fusion, thereby neglecting fundamental mechanisms underlying human 3D perception. To address this limitation, we introduce R3-PCQA (Ray-Reprojection-Reinforcement 3D Point Cloud Quality Assessor), a novel and principled framework that explicitly encodes perceptual priors into the assessment pipeline: A geometric-aware ray-based reprojection pipeline simulates viewpoint-dependent observation of 3D structure. A reinforcement-learning-based quality-salient subcloud selector adaptively attends to perceptually informative regions. The global view attention module aggregates local quality responses across viewpoints, forming a unified representation that facilitates reliable cross-view inference. Extensive experiments demonstrate that R3-PCQA achieves state-of-the-art performance on SJTU-PCQA, WPC, and WPC2.0.
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