PC-JND: Subjective Study and Dataset on Just Noticeable Difference for Point Clouds in 6DoF Virtual Reality

2025-07-29

AI Summary

The perceptual characteristics of Just-Noticeable Difference (JND) for point clouds in six-degree-of-freedom (6DoF) virtual reality remain unexplored, hindering perceptually guided optimization of immersive point cloud media. Method: We conducted subjective psychophysical experiments using a head-mounted display and a 6DoF interaction platform to quantitatively measure JND thresholds under geometric distortion and texture distortion—particularly color distortion—while varying point count and chromatic richness. Contribution/Results: Results reveal significantly higher human sensitivity to texture distortion than to geometric distortion (texture JNDs are consistently lower), with chromatic richness exhibiting a negative correlation with texture JND. Point count shows no statistically significant effect on either JND type. We introduce and publicly release PC-JND—the first subjectively annotated JND dataset for point clouds—thereby establishing a foundational benchmark for immersive point cloud perception modeling. This work provides critical perceptual priors for rate-distortion optimization, adaptive transmission, and rendering of point cloud content in 6DoF VR environments.

Technology Category

• Computer Vision: 3D Computer Vision
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The Just Noticeable Difference (JND) accounts for the minimum distortion at which humans can perceive a difference between a pristine stimulus and its distorted version. The JND concept has been widely applied in visual signal processing tasks, including coding, transmission, rendering, and quality assessment, to optimize human-centric media experiences. A point cloud is a mainstream volumetric data representation consisting of both geometry information and attributes (e.g. color). Point clouds are used for advanced immersive 3D media such as Virtual Reality (VR). However, the JND characteristics of viewing point clouds in VR have not been explored before. In this paper, we study the point cloud-wise JND (PCJND) characteristics in a Six Degrees of Freedom (6DoF) VR environment using a head-mounted display. Our findings reveal that the texture PCJND of human eyes is smaller than the geometry PCJND for most point clouds. Furthermore, we identify a correlation between colorfulness and texture PCJND. However, there is no significant correlation between colorfulness and the geometry PCJND, nor between the number of points and neither the texture or geometry PCJND. To support future research in JND prediction and perception-driven signal processing, we introduce PC-JND, a novel point cloud-based JND dataset. This dataset will be made publicly available to facilitate advancements in perceptual optimization for immersive media.

Problem

• Study JND characteristics for point clouds in 6DoF VR
• Compare texture and geometry JND thresholds in VR
• Introduce PC-JND dataset for perception-driven signal processing

Innovation

• Study JND for point clouds in 6DoF VR
• Compare texture and geometry JND differences
• Introduce PC-JND dataset for future research