New Challenges in Point Cloud Visual Quality Assessment: A Systematic Review (Dataset)

This dataset is a collection of annotated information on the scientific papers screened and analyzed for the systematic review of the literature in Point Cloud Visual Quality Assessment. 

The data is structured as follows:

General information

Document title

Authors

Year of publication

Venue (Conference or Journal title)

Citations (number)

URL/DOI

About the content 

Content Type: Point clouds (PC), Colored Point clouds (CPC), Meshes, Dynamic Point Clouds (DPC)

Content source: Source of the content used in a subjective QA test or the evaluation of one or more QA metrics

About metric benchmarks

Subjective Ground-truth Data: Dataset(s) Source of the subjective scores used as ground-truth in a QA metric benchmark

Assessed Metrics: Types of metrics assessed in a benchmark (JPEG standards, IQM, NR, State-of-the-art, others)

Performance Measures: PLCC, SROCC, KRCC, RMSE, OR, others

About Objective QA metrics

Metric: Name given to the metric introduced in this paper

Base: 3D-based or Projection-based

Categories: Categories that characterize the approach of the proposed metric (Feature-based, Learning-Based, Perceptual-based, IQM, others) 

Reference: Full-Reference (FR), Reduced-Reference (RR) or No-Reference (NR)

About Subjective QA experiments

Display: Type of display (2D, 3D, AR, MR, VR) and interaction approach (passive, interactive, 3DoF, 6DoF) used in the described experiment.

Rendering: Type of rendering used to display the stimuli (Points, Squares, Cubes, Surface)

Lab/Remote: The experiment was run in one or more lab environments, or remotely (Lab, Cross-Lab, Remote)

Rating: Subjective rating methodology used in the experiment (ACR, DSIS, PWC, others)

Dataset: Name of the new subjective dataset if the experiment's results were published.

Observers: Number of observers 

Distortion type: Types of distortions applied to the stimuli and assessed in the experiment