Point Cloud Quality Assessment
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Benchmarks
Most implemented
Asynchronous Feedback Network for Perceptual Point Cloud Quality Assessment
No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models
No-reference geometry quality assessment for colorless point clouds via list-wise rank learning
The Worse The Better: Content-Aware Viewpoint Generation Network for Projection-related Point Cloud Quality Assessment
CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality Assessment
No-Reference Point Cloud Quality Assessment via Graph Convolutional Network
Papers
PCQA-R1: Advancing Generalized 3D Point Cloud Quality Assessment with Reinforcement Learning
No-reference point cloud quality assessment (PCQA) has been an active topic in recent years and is used to measure and optimize the visual experience of point clouds. However, large multimodal models (LMMs) have rarely b…
Point Cloud Quality AssessmentReinforcement LearningPoint CloudsDAL-PCQA: Enabling Distortion-Level and Language-Driven Reasoning for Point Cloud Quality Assessment
Point Cloud Quality Assessment (PCQA) methods typically predict scalar Mean Opinion Scores (MOS), which quantify overall perceptual degradation but do not reveal its causes. In contrast, human observers naturally reason …
Point Cloud Quality AssessmentPoint CloudsPointQ-Bench: Benchmarking Diagnostic and Interpretable Point Cloud Quality Assessment
Point cloud quality plays a critical role in 3D acquisition, reconstruction, rendering, and perception, yet existing point cloud quality assessment (PCQA) research remains largely centered on scalar score prediction. In …
Point Cloud Quality AssessmentPoint CloudsGT-PCQA: Geometry-Texture Decoupled Point Cloud Quality Assessment with MLLM
With the rapid advancement of Multi-modal Large Language Models (MLLMs), MLLM-based Image Quality Assessment (IQA) methods have shown promising generalization. However, directly extending these MLLM-based IQA methods to …
Point Cloud Quality AssessmentImage Quality AssessmentQD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment
No-Reference Point Cloud Quality Assessment (NR-PCQA) still struggles with generalization, primarily due to the scarcity of annotated point cloud datasets. Since the Human Visual System (HVS) drives perceptual quality as…
Unsupervised Domain AdaptationPoint Cloud Quality AssessmentPoint CloudsUPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment
While no-reference point cloud quality assessment (NR-PCQA) approaches have achieved significant progress over the past decade, their performance often degrades substantially when a distribution gap exists between the tr…
Point Cloud Quality AssessmentDomain Adaptation