An open-source Unity-based dynamic point cloud player and paired-comparison platform for just-noticeable-distortion annotation

🔗 GitHub: https://github.com/Terriao/OpenDPC

Resources at a glance:
Prebuilt software → https://github.com/Terriao/OpenDPC/tree/main/Software
Preprocessing tool → https://github.com/Terriao/OpenDPC/tree/main/preprocess_tool
Distortion ladder configs → https://github.com/Terriao/OpenDPC/tree/main/ctc_configs
Test sequences → https://github.com/Terriao/OpenDPC/tree/main/test_data

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Background

A dynamic point cloud is a sequence of point clouds — one per frame — and the format has matured into a serious 3D representation for VR, autonomous-driving telemetry, volumetric telepresence, and immersive cultural-heritage capture. The visualisation tooling around it has progressed in fragments: real-time pipelines for tele-presence and social VR, capture-to-display systems for studio environments (Hofer et al., 2018), and web-based viewers tuned for industrial inspection (Mei et al., 2023). What remains underserved is a tool that pairs interactive offline playback with a reproducible subjective-evaluation module over a published distortion ladder, so that perceptual-quality results across labs become directly comparable.

OpenDPC targets exactly this gap. It is a Unity-based application that combines:

1. A dynamic point cloud player — frame-rate-correct, GPU-resident, interactive, looped, and built to handle full-length sequences without stuttering.
2. An integrated JND annotation module — a paired-comparison interface for locating the lowest distortion level which crosses the perceptual threshold, on a per-sequence per-subject basis.
3. A released subjective dataset — subjective quality evaluation involving 60 subjects collected with the module above.

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Position relative to prior work

Prior works on dynamic point cloud rendering methods include: Hofer et al. (IC3D 2018) built an end-to-end pipeline focused on capture-to-display latency for studio environments; Mei et al. (ICAICA 2023) released a web-based viewer optimised for industrial inspection of static and quasi-static parts; and the open-source cwipc library (used by VR2Gather, ACM MM 2024), which provides a C++/Unity stack for capturing, compressing, transmitting, and rendering point clouds in social-VR tele-presence applications, with its Unity package also capable of rendering pre-recorded sequences as a side feature of the capture pipeline.

OpenDPC sits in a different design point. It targets the offline, reproducible subjective-evaluation workflow: a researcher loads a reference sequence and a fixed distortion ladder, runs a structured paired-comparison protocol, and exports a JND result that another lab can independently reproduce. The components OpenDPC contributes that the systems above do not bundle are: a user-friendly dynamic point cloud playback interface, a paired-comparison JND module with a ternary-refinement controller, an integrated published V-PCC distortion ladder, and the accompanying 60-subject subjective dataset.

System	Primary purpose	Player	Built-in JND module	Public distortion ladder	Released subjective data
cwipc / VR2Gather	Live capture / social VR	✓	—	—	—
Hofer et al. (IC3D 2018)	Capture-to-display pipeline	✓	—	—	—
Mei et al. (ICAICA 2023)	Web viewer for manufacturing	✓ (static-friendly)	—	—	—
OpenDPC (this work)	Offline DPC playback + JND annotation	✓	✓	✓ (20-rate V-PCC)	✓ (60 subjects)

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Contents

1. Overview
2. System architecture
3. Repository layout
4. Getting started
5. The preprocessing tool — PontZen
6. The player in detail
7. The JND sub-platform in detail
8. Test sequences and V-PCC quality tiers
9. Subjective experiment and results
10. Use cases
11. Roadmap
12. Citation
13. Community
14. License
15. Acknowledgements
16. Contributors and contact

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Overview

	Component	One-line description
1	Dynamic point cloud processor (PontZen)	A standalone preprocessing tool that normalises geometry and bit-packs RGB-plus-luminance into a single 32-bit integer per point, ready for the GPU.
2	Dynamic point cloud player	Real-time looping playback of point cloud sequences with pause / resume, frame counter, configurable FPS, free rotation, and free zoom.
3	JND annotation sub-platform	A side-by-side reference-versus-distorted viewer with synchronised camera and a ternary search controller that converges on the perceptual threshold across a 20-rate distortion ladder.

The player and JND sub-platform live inside a single Unity application; the user picks Player Mode or JND Mode from a Home Panel at launch. PontZen runs separately, ahead of either mode.

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System architecture

┌──────────────────────────────────────────────────────────────────────┐ │ Raw dynamic point cloud sequences │ └─────────────────────────────────┬────────────────────────────────────┘ │ ┌───────────────▼───────────────┐ │ ❶ PontZen preprocessor │ │ · centring + unit-sphere │ │ scaling │ │ · 32-bit RGB+luminance pack │ └───────────────┬───────────────┘ │ GPU-ready frames ┌───────────────▼───────────────┐ │ Home Panel │ │ (mode selection) │ └───────────────┬───────────────┘ │ ┌───────────────────┴───────────────────┐ │ │ ┌──────────▼──────────┐ ┌────────────▼────────────┐ │ ❷ Player │ │ ❸ JND sub-platform │ │ Settings │ │ Settings │ │ ↓ │ │ ↓ │ │ Playing │ │ Playing │ │ · loop playback │ │ · paired view │ │ · pause / resume │ │ · synced camera │ │ · FPS control │ │ · ternary search │ │ · rotate / zoom │ │ · 6 s dwell timer │ └─────────────────────┘ └─────────────────────────┘

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Repository layout

OpenDPC/
├── .github                  # provide ISSUE_TEMPLATE and PULL_REQUEST templates
│   └── ISSUE_TEMPLATE/      # contain bug_report.yml, config.yml, feature_request.yml, and question.yml
│   └── PULL_REQUEST_TEMPLATE.md
├── Software/                # prebuilt Windows binary
│   └── software_v2.0.rar    # archive containing JNDModelStreamViewer.exe + Unity Data
├── preprocess_tool/         # standalone preprocessing executable
│   └── PontZen_v3.exe       # centring, unit-sphere scaling, 32-bit attribute packing
├── ctc_configs/             # V-PCC rate-point configurations (the 20-level distortion ladder)
│   ├── ctc-r01.cfg          # rate point r1   (Lossless)
│   ├── ctc-r02.cfg          # rate point r2   (Near-lossless)
│   │   …
│   ├── ctc-r07.cfg          # rate point r7   (CTC R1)
│   ├── ctc-r10.cfg          # rate point r10  (CTC R2)
│   ├── ctc-r13.cfg          # rate point r13  (CTC R3)
│   ├── ctc-r16.cfg          # rate point r16  (CTC R4)
│   ├── ctc-r19.cfg          # rate point r19  (CTC R5)
│   │   …
│   └── ctc-r20.cfg          # rate point r20  (Very-low quality)
├── test_data/               # 18 test sequences, ready for either Mode
├── logo.PNG                 # the project logo (used at the top of this README)
├── player.png               # player screenshot
├── jndviewer.png            # JND settings panel screenshot
├── jnd.png                  # paired-comparison view screenshot
├── samples.png              # 18-sequence thumbnail grid
└── README.md

• Prebuilt software: https://github.com/Terriao/OpenDPC/tree/main/Software
• PontZen preprocessing tool: https://github.com/Terriao/OpenDPC/tree/main/preprocess_tool
• V-PCC distortion-ladder configs (20 rate points): https://github.com/Terriao/OpenDPC/tree/main/ctc_configs
• Test sequences: https://github.com/Terriao/OpenDPC/tree/main/test_data

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Getting started

Prerequisites

A 64-bit Windows machine with a discrete GPU. For the JND module specifically, available VRAM must be large enough to hold both sequences (reference and one candidate distortion) at once — the module loads them simultaneously to keep paired-comparison latency below the rendering interval. A starting point: ≥ 4 GB VRAM for sequences denser than 10⁶ points per frame; more for higher-density content.

Install and run

1. Download Software/software_v2.0.rar from the repository.
2. Extract the archive with WinRAR / 7-Zip to any local folder.
3. Double-click JNDModelStreamViewer.exe to launch.

Preprocess a sequence with PontZen

The player expects sequences in a GPU-ready packed format produced by PontZen (see next section for the details). Workflow:

1. Download preprocess_tool/PontZen_v3.exe.
2. Place your raw per-frame point cloud files of one sequence in a single source folder (lexicographically ordered: 0001.ply, 0002.ply, …).
3. Run PontZen_v3.exe, point it at the source folder, and pick an output folder. PontZen processes each frame in turn.
4. Point the player (or the JND module's reference / distortion-ladder fields) at the output folder.

Choose a mode

At launch the Home Panel presents two entry points:

• Player Mode → opens the playback Settings Panel, where you point at one sequence folder and adjust FPS / scale, then click Start to enter the Playing Panel.
• JND Mode → opens the annotation Settings Panel, where you supply the reference folder and the distortion-ladder folder, configure viewing seconds / FPS / scale / search mode, then click Start to enter the paired-comparison Playing Panel.

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The preprocessing tool — PontZen

PontZen is a small Windows command-line / drag-and-drop executable that prepares raw point cloud sequences for GPU-resident playback. It does two things per frame, and only two things:

1. Geometric normalisation. Each frame's centroid is translated to the coordinate origin, then the frame is isotropically rescaled until it fits inside the unit sphere of radius 1. This removes the dependence of downstream viewing parameters (camera distance, model scale) on the absolute capture units, so a sequence shot in millimetres and one shot in metres render at the same apparent size.

2. Attribute bit-packing. Each point's four attribute channels — Red, Green, Blue, and a derived luminance — are encoded into a single 32-bit unsigned integer:

   Bits	0 – 7	8 – 15	16 – 23	24 – 31
   Channel	R	G	B	Luminance

   Packing four 8-bit channels into one 32-bit integer halves the per-point bandwidth on upload and, more importantly, allows the entire preprocessed sequence to be loaded into VRAM in one pass and indexed per frame at playback time without further copies. The arithmetic cost (one shift-and-mask per channel per shader invocation) is negligible.

The packed output is what the player ingests. Without preprocessing, raw frames work for the very smallest sequences but break the GPU-streaming guarantees on anything realistic.

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The player in detail

The player is built around three design constraints we found missing in existing tools:

1. Streaming-friendly memory. The packed format produced by PontZen lets the full sequence sit in VRAM and be indexed per frame at playback time. The arithmetic on the GPU side is cheap; the memory bandwidth saved is real.

2. Camera-anchored model. Each frame is rendered onto a single empty model placed at the camera origin, which means rotation and zoom interact intuitively with the model rather than with the world. Pause does not freeze the camera — you can keep inspecting the geometry from any angle while a frame holds.

3. Frame counter + adjustable FPS. A persistent overlay shows the current frame index and the total length of the sequence. The settings panel lets you change the playback rate without restarting the viewer.

Interactive controls during playback:

Action	Input
Pause / resume	play/pause icon
Rotate model	Left-drag
Zoom	Scroll wheel
Change FPS	Settings panel (live)

Format support. The current release accepts the standard point cloud frame format on input to PontZen (.ply); broader importers (.pcd, .las, .e57, and on-the-fly .obj conversion) are on the roadmap, and contributions are welcome.

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The JND sub-platform in detail

Configuration

The configuration screen exposes the parameters that previous JND-on-video studies have shown to dominate inter-subject variance:

Parameter	Default	What it controls
Viewing seconds	6 s	Minimum time the subject must observe each comparison before the verdict buttons unlock. Prevents reflex clicks and gives temporal masking time to settle.
FPS	15 fps	Playback rate during evaluation. Lower than typical real-time playback to keep per-frame attention high.
Model scale	3×	Apparent size of the model. Held constant so that retinal projection is comparable across subjects and sessions.
File directory	—	A parent folder of subfolders, one per rate point, sorted by ascending distortion.
Result file	—	The path to save the subject's JND results.
Search mode	Binary search	Selects the controller that walks the rate points (see below).

The viewer

The viewer shows the pristine sequence on the left and the candidate distorted sequence on the right. Crucially, the two cameras are linked: any rotation or zoom applied to one side is mirrored on the other, so the subject is never comparing apples and oranges at different angles. Two verdict buttons sit between the panels: Similar and Different.

The ternary-search controller

Terminology note. The "interval" walked by the controller is a rate-point interval along the V-PCC distortion ladder — not a temporal interval within the sequence. Every comparison shows the full sequence from start to end; the controller only changes which rate point on the ladder gets paired against the reference.

Locating the JND boundary in a twenty-point distortion ladder by exhaustive comparison would need twenty trials per subject per sequence. We use a ternary refinement instead of a pure bisection, which is gentler on the noisy verdicts that subjective experiments inevitably produce:

Initial rate-point interval:  [r_lo, r_hi]  =  [r1, r20]
Loop until |r_hi - r_lo| ≤ 1:
    r_mid ← round( (r_lo + r_hi) / 2 )         the midpoint rate point on the ladder
    show paired comparison: reference  vs  candidate-at-r_mid (full sequence, both sides)
    wait for verdict (button unlocks after 6 s)
    if verdict = "Similar":      r_lo ← r_lo + ⌈(r_hi - r_lo) / 3⌉
                                 (drop the lower-rate, higher-quality third — still imperceptible there)
    if verdict = "Different":    r_hi ← r_hi - ⌈(r_hi - r_lo) / 3⌉
                                 (drop the higher-rate, lower-quality third — threshold lies below r_mid)
Output: r_lo of the final interval  =  this subject's JND rate point for this sequence

Trimming a third rather than a half at each step costs a small number of extra comparisons relative to plain bisection — but the surplus dampens the noise that comes from low-confidence verdicts near the threshold, and the rate point the controller settles on lands closer to the true JND across our subject pool.

Internally, the controller maintains a binary-tree representation of the visited rate-point intervals (PointCloudBinaryTreeNodes), so the full traversal of any session can be replayed post-hoc from the log file.

Outputs

After a JND session finishes, the module writes two artefacts:

• Result file — a per-subject record of the converged JND rate point for every sequence in the run, written through a native Windows save-file dialog.
• Session log — a rolling text log under <install>/Logs/, capped at the most recent N files (FileLogger). Useful for re-tracing a subject's verdict sequence or diagnosing UI / asset-loading issues after the fact.

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Test sequences and V-PCC quality tiers

The test data contains eighteen dynamic models sourced from Sketchfab, covering objects, characters, and animals (see thumbnails below). For each model, the first 64 frames are encoded by V-PCC at the twenty rate points defined in ctc_configs/, spanning nine quality tiers — from lossless down to very-low-quality. Five of the twenty rate points align with the MPEG V-PCC Common Test Conditions (CTC R1 through R5).

V-PCC rate-point configuration

Each row of the table corresponds to one .cfg file in ctc_configs/.

Rate point	Config	occupancyPrecision †	Geometry QP	Attribute QP	Quality tier
r1	ctc-r01.cfg	2	−12	0	Lossless
r2	ctc-r02.cfg	2	−6	6	Near-lossless
r3	ctc-r03.cfg	2	0	9	Near-lossless
r4	ctc-r04.cfg	2	4	12	High fidelity
r5	ctc-r05.cfg	2	8	16	High fidelity
r6	ctc-r06.cfg	2	12	20	High fidelity
r7	ctc-r07.cfg	4	16	22	High quality · CTC R1
r8	ctc-r08.cfg	4	17	23	High quality
r9	ctc-r09.cfg	4	18	24	High quality
r10	ctc-r10.cfg	4	20	27	Medium-high quality · CTC R2
r11	ctc-r11.cfg	4	21	29	Medium-high quality
r12	ctc-r12.cfg	4	22	30	Medium-high quality
r13	ctc-r13.cfg	4	24	32	Medium quality · CTC R3
r14	ctc-r14.cfg	4	25	33	Medium quality
r15	ctc-r15.cfg	4	26	34	Medium quality
r16	ctc-r16.cfg	4	28	37	Medium-low quality · CTC R4
r17	ctc-r17.cfg	4	29	38	Medium-low quality
r18	ctc-r18.cfg	4	30	39	Medium-low quality
r19	ctc-r19.cfg	4	32	42	Low quality · CTC R5
r20	ctc-r20.cfg	4	36	48	Very-low quality

† occupancyPrecision is the V-PCC reference-encoder occupancy-map precision parameter — a dimensionless block-size index (geometry block precision in voxel units of the underlying grid), not a physical distance in mm or cm. The Geometry-QP and Attribute-QP columns are standard V-PCC quantisation parameters.

Total compressed asset size: 18 sequences × 20 rate points × 64 frames = 23,040 distorted frames ready for evaluation.

Asset preparation pipeline

Source .glb models from Sketchfab were converted to colour-bearing point cloud sequences in two stages:

.glb (Sketchfab)  ──Blender──►  .obj + textures  ──CloudCompare batch──►  point cloud sequence

Sampling method. The .obj → point cloud conversion uses Poisson-disk sampling to produce a roughly uniform point density across the surface (cf. uniform random sampling, which under-samples low-curvature regions). For very fine geometry (sequences F, M) we additionally cap the per-frame point count at 100 K to keep playback responsive on mid-range GPUs. The choice of sampler does affect downstream JND thresholds — uniform random sampling typically yields a noisier surface and slightly lower (more sensitive) JND values; Poisson-disk gives the most stable thresholds in our pilot study. The script is parameterised, so switching sampler is a one-flag change.

The output of this pipeline is then passed through PontZen before being loaded into the player.

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Subjective experiment and results

Protocol

We split the 18 sequences into two non-overlapping groups (A–I and J–R) and recruited sixty volunteers, thirty per group. The pool mixed multimedia-research-trained subjects with naive participants to keep generalisation honest. Before any data was collected, every subject went through a calibration session covering the protocol, the interface, and the verdict semantics; subjects could request a break at any point during the actual experiment to reduce visual fatigue.

Outlier verdicts were trimmed under the ITU-R BT.500-13 screening rule before averaging.

Per-subject JND results, group 1 (sequences A–I, subjects 1–30)

Seq	s1	s2	s3	s4	s5	s6	s7	s8	s9	s10	s11	s12	s13	s14	s15	s16	s17	s18	s19	s20	s21	s22	s23	s24	s25	s26	s27	s28	s29	s30	Mean	Std
A	20	16	16	9	12	19	19	14	18	19	5	13	8	20	8	12	12	16	7	13	15	16	18	20	9	18	10	12	7	8	13.63	4.60
B	16	13	13	15	9	7	16	13	13	14	7	11	12	10	12	7	9	12	9	9	9	9	9	16	7	7	5	9	5	9	10.40	3.20
C	13	12	16	16	6	14	19	7	9	14	10	14	14	20	16	7	7	14	20	9	14	7	7	19	9	13	12	12	7	12	12.30	4.21
D	16	11	15	13	14	13	16	12	7	13	9	15	7	9	15	7	9	16	11	13	13	7	7	13	14	12	12	12	12	8	11.70	2.97
E	13	5	9	14	7	14	18	16	7	14	7	14	14	14	16	7	7	16	9	9	9	7	7	13	11	9	7	7	5	7	10.40	3.84
F	16	5	7	14	12	16	16	14	10	16	9	13	11	15	9	10	9	14	7	9	14	7	9	13	12	16	9	12	7	7	11.27	3.36
G	19	9	14	16	20	18	20	16	15	20	7	16	20	20	18	13	9	19	18	19	14	5	7	16	11	19	9	18	12	12	14.97	4.57
H	13	6	12	14	16	14	16	16	13	16	5	12	12	20	12	9	12	12	14	9	15	9	7	19	9	14	10	13	9	9	12.23	3.58
I	13	9	18	16	14	16	18	19	14	19	14	16	16	19	14	14	8	15	10	13	16	7	16	18	16	14	12	16	9	9	14.27	3.40

Per-subject JND results, group 2 (sequences J–R, subjects 31–60)

Seq	s31	s32	s33	s34	s35	s36	s37	s38	s39	s40	s41	s42	s43	s44	s45	s46	s47	s48	s49	s50	s51	s52	s53	s54	s55	s56	s57	s58	s59	s60	Mean	Std
J	16	13	15	16	15	16	16	13	11	18	13	14	16	18	16	18	7	18	15	13	16	12	12	18	15	18	16	16	12	16	14.93	2.55
K	20	7	8	16	11	15	18	13	11	15	13	6	17	10	16	16	14	10	15	16	13	20	6	7	5	18	18	19	6	14	13.10	4.58
L	20	10	16	17	14	14	16	16	9	19	13	7	9	10	18	14	13	7	9	9	12	20	7	7	7	19	16	12	9	13	12.73	4.27
M	20	9	13	18	8	14	16	13	13	9	9	7	17	7	19	16	9	5	7	16	15	12	7	9	5	19	18	16	15	16	12.57	4.60
N	18	10	16	16	15	17	16	13	18	18	9	13	14	13	18	15	12	14	16	13	13	12	9	9	12	18	16	16	9	12	14.00	2.95
O	16	9	2	17	14	18	16	16	16	18	13	12	5	12	16	15	13	14	16	13	11	12	7	13	7	19	15	16	9	16	13.20	4.06
P	13	14	10	16	14	16	18	18	14	19	13	13	16	9	16	16	12	18	13	14	12	12	14	14	16	18	16	18	13	14	14.63	2.48
Q	16	8	2	17	16	13	16	18	17	15	13	9	15	9	18	16	13	17	18	12	15	16	7	16	5	16	15	16	10	16	13.67	4.13
R	13	5	15	16	16	17	18	13	18	19	13	14	15	9	18	16	10	18	16	14	16	16	7	12	7	16	16	16	7	15	14.03	3.77

What the data tells us

The JND clusters in the middle of the ladder. Across every one of the 18 sequences the mean JND falls inside the r10–r15 band — the transition from medium-high to medium quality. Below r10 the eye is forgiving; above r15 it is unforgiving; right in between is where the threshold lives.

This has a direct codec-side implication. If a V-PCC encoder picks any rate point above r15 for a perceptually-lossless target, it is leaving bandwidth on the floor — distortion at r15 is already invisible to most observers. If it picks anything below r10, it is buying marginal bitrate savings against visible quality loss. The r10–r15 band is the sweet spot for maximally aggressive yet perceptually-safe V-PCC compression.

Low-motion sequences yield tighter agreement. Sequences D, J, P, and N show the smallest inter-subject standard deviation — they also happen to be the four sequences with the smallest frame-to-frame motion. With less motion, temporal masking weakens, the JND threshold depends less on each viewer's tracking strategy, and verdicts converge. A practical consequence: high-motion content needs more subjects per study to reach the same confidence as low-motion content, and any future JND model for dynamic point clouds should be evaluated separately on high- and low-motion subsets.

On choice of perceptual signal. Our current evaluation uses overall V-PCC distortion as the visibility variable, not a temporal-artifact-specific signal (e.g., flicker, surface popping, or seam discontinuities under V-PCC patch updates). This is a deliberate scoping choice: V-PCC distortion is the signal that codec implementers actually tune, so the published JND thresholds are immediately actionable for rate-control work. Adding a dedicated temporal-artefact JND track (variant comparisons that hold spatial quality fixed and vary temporal artefact severity) is one of the main items on the roadmap.

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Use cases

OpenDPC has been built with three downstream applications in mind:

1. Rate-distortion calibration for dynamic point cloud codecs. Use the JND results to set perceptually-meaningful target bitrates instead of arbitrary PSNR points.
2. Subjective ground truth for point cloud quality assessment. Pair the JND ladder with objective quality scores to train and validate PCQA models.
3. Demos and pedagogy. The player on its own is a clean demo platform for showcasing dynamic point cloud capture, compression, or rendering algorithms.

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Roadmap

• Cross-platform builds — the current public release is Windows-only). The Unity codebase is portable; macOS and Linux build profiles are on the immediate roadmap, blocked only on substituting Standalone File Browser for the native Windows shell calls.
• PontZen cross-platform — porting the preprocessor to macOS / Linux follows the player port; the core normalisation + bit-packing logic is itself portable.
• Broader format support — importers for .pcd, .las, .e57, and on-the-fly .obj conversion, removing the current input-format restriction in the PontZen stage.
• GPU-streamed long sequences — current VRAM-resident pipeline caps sequences at the GPU's free memory; a sliding-window streamer is in development.
• Automated JND batch mode — head-mounted display integration and automated session orchestration to scale subjective studies.
• Beyond V-PCC — distortion ladders generated by G-PCC and by emerging learned codecs.
• Public mirror on OpenI — synchronised mirror at Peng Cheng Laboratory's OpenI platform for users behind GitHub-restricted networks.

---

Citation

If OpenDPC supports your research, please cite the companion paper:

@inproceedings{gao2026opendpc,
  title     = {OpenDPC: An Open Source Dynamic Point Cloud Player and Platform
               for Just Noticeable Distortion},
  author    = {Gao, Wenxu and Fan, Songlin and Gao, Wei},
  booktitle = {Proceedings of the 34th ACM International Conference on Multimedia},
  year      = {2026},
  note      = {Under review}
}

When you build on V-PCC results, please also cite the MPEG V-PCC standard (Graziosi et al. 2020) and the BT.500-13 protocol (ITU-R 2012).

---

Community

OpenDPC is released under the MIT License and hosted on GitHub.

We welcome:

• New test sequences (please bring their Sketchfab / source licence)
• Distortion-ladder generators for codecs other than V-PCC
• Cross-platform builds (macOS, Linux) and the file-dialog refactor that unblocks them
• Importers for additional point cloud formats (.pcd, .las, etc.)
• Bug reports, especially around long sequences, large attributes, and edge cases

Open an issue first for non-trivial contributions so we can align on interfaces. Pull requests are merged after review.

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License

Source code is released under the MIT License. Encoded V-PCC distortion ladders are released under the same license, subject to the individual Sketchfab licences of the underlying 3D models. The Unity engine itself is governed by Unity's standard licensing terms — please consult Unity Technologies for redistribution constraints on engine binaries.

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Acknowledgements

OpenDPC builds on the work of the MPEG V-PCC standardisation activity, the ITU-R BT.500-13 subjective-evaluation methodology, the Unity engine, and the dynamic 3D modelling community that contributes content to Sketchfab. We acknowledge prior open and academic work on dynamic point cloud visualisation, including the cwipc library and its social-VR application VR2Gather (Jansen et al., ACM MM 2024), Hofer et al. (IC3D 2018), and Mei et al. (ICAICA 2023) — OpenDPC complements these systems by occupying a different design point (offline reproducible JND evaluation).

We thank the sixty subjects who participated in the JND study, and Peng Cheng Laboratory together with the OpenI platform for compute resources and the planned public mirror of the repository.

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Contributors and contact

Role	Name	Affiliation
Coordinator	Asso. Prof. Wei Gao	School of Electronic and Computer Engineering, Peking University · Peng Cheng Laboratory
Lead developer	Wenxu Gao	School of Electronic and Computer Engineering, Peking University · Peng Cheng Laboratory
Contributor	Songlin Fan	Institute of Trustworthy Embodied AI, Fudan University · China Mobile Shanghai ICT Co., Ltd.

For questions, collaboration, OpenI mirror access, or push privileges, please contact:

Asso. Prof. Wei Gao — gaowei262@pku.edu.cn

Bug reports and feature requests are tracked via GitHub Issues.

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_{OpenDPC · Built at the Wei Gao group, Peking University and Peng Cheng Laboratory.}