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REVIEW 2 major objections 6 minor 36 references

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

T0 review · 2 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper reports the first point-cloud just-noticeable-difference measurements in 6DoF virtual reality: texture distortion becomes visible at a smaller V-PCC quantization step than geometry distortion for most point clouds.

desk verdict First point-cloud JND dataset for 6DoF VR, but the headline claim that texture JND is smaller than geometry JND overstates what an uncalibrated QP comparison can support. read the letter →

arxiv 2507.21557 v1 pith:RY4NY3GJ submitted 2025-07-29 cs.MM

Chunling Fan , Yun Zhang , Dietmar Saupe , Raouf Hamzaoui , Weisi Lin This is my paper · ORCID
classification cs.MM
keywords justnoticeabledifferencepointclouds6DoFvirtualrealityV-PCCsubjectivequalityassessmentsatisfieduserratiocolorfulnessJNDdataset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to measure, for the first time, the point-cloud just noticeable difference (JND, the minimum distortion at which a difference becomes visible) in a six-degrees-of-freedom virtual-reality environment, and to publish the resulting thresholds as a reusable benchmark. Using 34 reference point clouds and a pool of 68 viewers on a head-mounted display, it finds that, with V-PCC quantization as the distortion scale, texture distortion becomes visible at a smaller quantization step than geometry distortion for most clouds. The paper also reports that the texture threshold grows with the colorfulness of the cloud, while geometry thresholds show no such link. These results give compression systems a content-dependent target: colorful clouds should spend proportionally more bits on color, and geometry and texture should be treated as separate perceptual budgets.

What carries the argument

The argument is carried by the PCJND protocol: side-by-side display of a reference and a distorted point cloud in a 6DoF virtual room, a relaxed binary search that narrows the QP search range by three quarters at each step, and per-reference aggregation of the resulting integer thresholds. V-PCC (Video-based Point Cloud Compression) generates the distortions by projecting each cloud into geometry and texture videos that are compressed with HEVC, and the QP of those videos is the distortion step. A satisfied-user-ratio (SUR) curve is fitted per reference as the complementary cumulative distribution function of the normal distribution fitted to the collected PCJND samples, and the PCJND is the smallest QP at which the SUR reaches a chosen threshold T (85%, 75%, 65%, or 50%). Texture PCJND and geometry PCJND come from the same protocol with only texture or only geometry compressed, so the comparison in QP units is the central instrument of the paper.

What would settle it

Re-test the same 34 clouds with distortion levels matched on a common scale, for example equal bitrate or equal PSNR for texture and geometry, and check whether texture PCJND remains below geometry PCJND for most references; if the ordering flips or dissolves, the headline comparison is an artifact of QP units.

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Extended reading notes

Core claim

Working in a 6DoF VR room where subjects could walk around and view point clouds from all sides, the study measured the point-cloud-wise just noticeable difference (PCJND) separately for texture and for geometry, using V-PCC compression levels as the step size. Distortion levels were generated by varying the texture QP from 1 to 51 while leaving geometry lossless, and vice versa. For each reference, a relaxed binary search located each subject's threshold, outlier subjects and samples were removed, and a satisfied-user-ratio (SUR) curve was fitted as the complement of a normal cumulative distribution, from which 85%, 75%, 65%, and 50% PCJND values were read. At the 85% SUR threshold, 28 of 34 references had a smaller texture PCJND than geometry PCJND, and the mean texture PCJND was smaller at every threshold; paired t-tests rejected equal means at 65%, 75%, and 85%. The texture PCJND correlates with colorfulness (r = 0.5655), whereas geometry PCJND does not, and the number of points correlates with neither. The resulting PC-JND dataset contains 34 references and 51 V-PCC-distorted versions of each, with per-reference threshold labels, and is being made publicly available.

Load-bearing premise

The load-bearing premise is that one V-PCC quantization step on the texture scale and one on the geometry scale are perceptually comparable units, so that a smaller texture PCJND can be read as texture errors being noticed first.

Editorial extensions

If this is right

  • A point-cloud encoding pipeline can treat texture and geometry as separate perceptual budgets, allowing higher geometry QP than texture QP before visible degradation for most content.
  • Colorfulness gives a cheap content feature for predicting texture PCJND, potentially replacing or initializing per-content subjective tests.
  • The four SUR thresholds (85%, 75%, 65%, 50%) let applications choose an operating point according to how sure they need to be that no viewer notices a difference.
  • PC-JND provides a benchmark for training objective PCJND predictors and for validating point-cloud quality metrics under 6DoF HMD viewing.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the paper compares texture and geometry QP directly, an obvious extension is to recalibrate QP to a common distortion metric and re-test whether the ordering survives.
  • Real V-PCC encodes geometry and texture jointly, so testing mixed distortions could reveal masking between the two distortion types that this separate-distortion design cannot capture.
  • The colorfulness correlation suggests that a rendered-image-based colorfulness feature, rather than raw point statistics, might predict texture PCJND; this is testable on the released dataset without new viewing experiments.
  • The lack of correlation with point count may not generalize to sparser clouds; a synthesis experiment with matched geometry error across point densities could separate density from visible surface detail.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. This paper presents a subjective study of point-cloud-wise just noticeable difference (PCJND) for V-PCC-compressed point clouds viewed in a 6DoF VR environment. The authors select 34 reference point clouds, encode each with V-PCC at 51 texture-QP levels (Part I) and 51 geometry-QP levels (Part II), and collect binary difference judgments from 68 subjects using a relaxed binary search with side-by-side reference/distorted pairs in an HTC Vive environment. After outlier removal, PCJND samples are fit to normal distributions, SUR curves are derived, and PCJND thresholds are reported at 50%, 65%, 75%, and 85% SUR. The paper claims that texture PCJND is smaller than geometry PCJND for most references, that texture PCJND correlates positively with colorfulness, and that there is no clear correlation between colorfulness and geometry PCJND or between the number of points and either PCJND. The PC-JND dataset is introduced as a public benchmark.

Significance. If the findings hold, this is a useful first benchmark for point-cloud JND in immersive VR: the dataset of 34 references and 51 distortion levels per reference fills a clear gap, and the subjective protocol with HMD/6DoF viewing, outlier screening, and SUR analysis is broadly sound and worth publishing as a measurement contribution. The content-dependent SUR behavior and the colorfulness-texture correlation are interesting, falsifiable observations. The main weakness is that the headline comparison between texture and geometry JND is made on uncalibrated V-PCC QP scales, so the perceptual ordering does not follow from the data as presented. The dataset and measurement methodology remain valuable independent of that cross-modality comparison.

major comments (2)
  1. [Section V-B, Table III] The central claim that texture PCJND is smaller than geometry PCJND (Abstract; Section V-B; Table III) compares fitted thresholds on the V-PCC QP scale across two different distortion types. QP is an encoder parameter, not an established perceptual scale, and there is no evidence that a one-step change in texture QP produces the same amount of visible distortion as a one-step change in geometry QP. The paper itself hedges in Section V-B ('if QP is used to measure distortion'), but the Abstract and Conclusion present the comparison without this qualification. Please either calibrate the two QP scales to a common unit (e.g., bitrate, D1/color PSNR, PCQM, or a direct behavioral anchor) or reframe the result as two separate JND measurements without a cross-modality ordering.
  2. [Section III-A, Section V-B] The 'geometry-only' condition in Part II may not be cleanly geometry-only. In V-PCC, geometry compression changes the reconstructed point positions onto which color attributes are mapped, so the condition with geometry QP variation and uncompressed texture video can still introduce texture/color artefacts at the rendered surface. The paper should quantify the resulting color distortion (e.g., color PSNR between the reference and the geometry-only reconstructions) or otherwise demonstrate that texture distortion is negligible, before interpreting the geometry PCJND as purely geometric sensitivity.
minor comments (6)
  1. [Section III-A] The sentence 'the point clouds used in the test are listed in TableI' appears to reference the wrong table; the reference point clouds are listed in Table II, while Table I summarizes existing JND datasets.
  2. [Section III-B] There are several typos and inconsistent names: 'THC Vive' should be 'HTC Vive'; 'Unversity' should be 'University'; 'Scuptures' appears as 'Sculptures'; 'the20smaria' and 'ulliwegner' in Table II are written as 'the20sMaria' and 'UliWegner' in the text; 'A VS' is likely 'AVS'.
  3. [Section V-B, Table III] The PSNR columns in Table III are not clearly defined: the text mentions 'MSE, PSNR (p2point)' but the table has no MSE column, and it is not stated whether the texture PSNR is computed on color attributes and the geometry D1 on point positions. Please specify the exact metrics and their meaning at the 75% PCJND operating point.
  4. [Section III-C] Please clarify how the 68 subjects were assigned to the two parts and sessions; the text says 68 subjects were involved, but after outlier removal only 33 geometry and 35 texture samples per reference remain, and Table I lists 33 in the 'No. rates/seq' column. State explicitly how many valid subjects rated each reference in each part.
  5. [Section V-C] The correlation analysis reports coefficients but no p-values, confidence intervals, or correction for the multiple correlations tested. Please add significance tests so that the statements 'texture PCJND correlates with colorfulness' and 'there is no significant correlation' are backed by inferential statistics.
  6. [Section V-D] The Limitations subsection addresses response bias but does not discuss the QP-scale comparability issue raised above; an explicit acknowledgment would help readers interpret the texture-versus-geometry comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PCJND thresholds are measured subjectively; the SUR fit and correlations are post hoc statistics, and no claim reduces to a fitted parameter or self-citation.

full rationale

The paper's derivation chain starts from subjective YES/NO judgments per subject and reference, collected with a relaxed binary search. The central threshold (PCJND) is defined as the QP value at a given Satisfied User Ratio threshold, and the SUR curves are fitted from the collected per-subject thresholds using a normal CDF model. This is statistical estimation from the data, not a model that presupposes the outcome. The texture-vs-geometry comparison is a direct comparison of fitted QP thresholds; while the uncalibrated QP-scale comparability is a substantive validity threat (one QP step may not correspond to equal perceptual distortion for texture vs geometry), that is not circularity because the thresholds themselves are not derived from the hypothesis being tested. The colorfulness and point-count correlations are post hoc and do not feed back into threshold derivation. Self-citations occur (Fan et al. for stereo JND methods, Lin et al. for SUR prediction), but none is load-bearing for the novel point-cloud measurements, which are self-contained and dataset-driven. Therefore no step reduces by construction to its inputs.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The analysis rests on standard psychometric assumptions and one ad hoc assumption (QP comparability) that is load-bearing for the central claim. No new theoretical entities are introduced.

free parameters (2)
  • normal distribution mean and std per reference = per-reference values in Table III and fitted SUR curves
    The PCJND samples are modeled as a normal distribution; mean and std are estimated from the subjective data and used to derive the 85%, 75%, 65%, 50% PCJND thresholds. These are statistical fits, not theoretical constants.
  • outlier detection thresholds (range > 3, std > 1) = r_m > 3, sigma_m > 1
    Chosen by hand following VideoSet [22] to remove outlier subjects. Not derived from first principles.
assumptions (4)
  • domain assumption PCJND samples for each reference are normally distributed
    Invoked in Section V(a) to justify fitting SUR curves with a normal distribution. The Jarque-Bera test fails for 2 of 34 references in Part I and 6 of 34 in Part II, but these references are still modeled with a normal distribution.
  • ad hoc to paper QP values for texture and geometry compression are directly comparable
    The central claim that texture PCJND is smaller than geometry PCJND relies on comparing QP thresholds. No calibration between texture and geometry QP distortion scales is provided.
  • standard math The relaxed binary search converges to the true JND threshold
    Adopted from VideoSet [22]; assumed to provide unbiased threshold estimates.
  • domain assumption Side-by-side comparison in 6DoF VR with free navigation is a valid JND protocol
    The experimental setup in Section III-C uses this method; subjects can walk around and view from any angle, which may introduce variability in viewing distance and angle not controlled in the analysis.

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Cite this review

Pith. "Pith review of PC-JND: Subjective Study and Dataset on Just Noticeable Difference for Point Clouds in 6DoF Virtual Reality." pith.science (2026). https://pith.science/paper/RY4NY3GJ

@misc{pith2026250721557,
  author       = {Pith},
  title        = {Pith review of: PC-JND: Subjective Study and Dataset on Just Noticeable Difference for Point Clouds in 6DoF Virtual Reality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RY4NY3GJ}},
  note         = {Machine review of arXiv:2507.21557}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2507.21557 by the authors.

Figure 1
Figure 1. Snapshots of the point clouds used in our subjective test. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Distribution of colorfulness and the number of points in the point clouds. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The reference was encoded using texture and geometry [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: 6DoF VR Environment and rendering views. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Evaluation flowchart. Inspired by [22], a subject will be detected as an outlier if both of its range rm and standard deviation σm are large. To be specific, the subject is outlier if rm > 3 and σm > 1. As shown in Figures 6(b) and (d), three subjects (subject 1, 25, a…
Figure 6
Figure 6. Figure 6: Boxplot and range vs. standard deviation of the standard scores. (a) boxplot for texture PCJND samples. (b) range vs. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: SUR curve, 75% and 50% PCJNDs of point cloud [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: SUR as a function of the (a) texture QP, (b) geometry QP. Each figure corresponds to one category of point clouds. [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Fitting models of the histogram of texture and geometry [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Correlation between colorfulness, number of points and PCJND. (a) colorfulness vs. texture PCJND. (b) colorfulness [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

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Reference graph

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.