PVINet: Point-Voxel Interlaced Network for Point Cloud Compression

Debin Zhao; Xiandong Meng; Xiaopeng Fan; Xingtao Wang; Xuan Deng

REVIEW 4 major objections 5 minor 28 references

PVINet: Point-Voxel Interlaced Network for Point Cloud Compression

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read PVINet claims that a point-voxel interlaced encoder, with interactions at each scale, preserves both global structure and local detail in learned point cloud compression and cuts bit rate versus prior learned codecs.

desk verdict Solid hybrid point-voxel compression architecture with real but modest gains; the conditional-convolution contribution is plausible but not yet isolated from added capacity, and the routing side channel is weaker than claimed. read the letter →

arxiv 2509.01097 v1 pith:LDPRF75A submitted 2025-09-01 cs.CV

Xuan Deng , Xingtao Wang , Xiandong Meng , Xiaopeng Fan , Debin Zhao This is my paper · ORCID

classification cs.CV

keywords pointcloudcompressionlearnedlossycodingsparseconvolutionpoint-voxelinterlacedarchitectureconditionalgeometryrate-distortionoptimizationroutingweights

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

This paper proposes a learned lossy point cloud geometry codec whose encoder runs a voxel branch and a point branch in parallel, not one after the other. At each scale the two branches exchange features: the voxel side contributes global structure to the point side, while the point side customizes voxel convolution kernels through a conditional sparse convolution. Only the small routing weights from that customization, not the full point embeddings, are transmitted to the decoder. On dense test point clouds the authors report 15.5% and 8% bit-rate savings versus PCGCv2 and DeepPCC under point-to-point PSNR, with larger savings over G-PCC and GRASP-Net. The practical interest is that a learned codec can keep fine local detail without paying the bit cost of transmitting point-level features.

What carries the argument

The conditional sparse convolution is the load-bearing mechanism. Instead of using a fixed kernel, it pools point features, runs an MLP and softmax to produce routing weights, and forms the effective kernel as a weighted sum of expert kernels. Because the weights are derived from point embeddings and are transmitted to the decoder, the network's kernels change with each input point cloud and the decoder receives a low-cost description of local geometry. The interlaced encoder is the supporting structure: voxel-to-point interaction uses trilinear interpolation to add structural features to point features, and point-to-voxel interaction feeds point features into the conditional sparse convolut

What would settle it

Train or run PVINet with the routing weights replaced by constant or random weights at decode time while keeping everything else identical. If D1 PSNR at matched bit rates stays essentially the same, the conditional convolution is not what drives the gains. A second check is to count the actual bits consumed by routing weights in the bitstream and verify they are negligible as claimed.

Watch

Extended reading notes

Core claim

The central claim is that point cloud geometry reconstruction improves when global and local information are captured simultaneously and made to communicate at every scale, rather than processed in a fixed cascade. PVINet does this with a voxel encoder that sees the whole occupied volume and a point encoder that builds local graphs around voxel centers. A conditional sparse convolution turns pooled point features into routing weights that linearly combine a set of expert convolution kernels; the decoder uses the same weights as guides for conditional sparse and transposed sparse convolutions. The authors report that the interlaced structure alone gives a 10.3% BD-rate gain over the voxel-onl

Load-bearing premise

The single routing-weight vector derived from pooled point features carries enough local geometry that a decoder seeing only voxel features plus those weights can restore fine details; if this side channel is too weak, the reported gains vanish.

Editorial extensions

If this is right

If the central claim holds, learned geometry codecs can model global structure and local detail jointly without sending dense point embeddings, so the bit budget stays close to voxel-level coding while reconstruction quality improves.
The routing-weight side channel is nearly free in bits, suggesting the same decoder-guidance trick can be reused anywhere a compact conditioning signal is needed.
The reported ablation numbers separate the gains: interlacing accounts for 10.3% BD-rate improvement over voxel-only coding, and conditional sparse convolution adds 7.7%.
The method's competitive position on the twelve dense human point clouds used in the evaluation is stated as a 15.5% D1 and 15.4% D2 saving over PCGCv2, and 8% D1 and 6.1% D2 saving over DeepPCC.
The reported loss relative to SparsePCGC is attributed by the authors to SparsePCGC's multistage lossless thumbnail coding rather than to the interlaced representation; if correct, closing that gap is an engineering direction, not a refutation of the architecture.

Reading between the lines

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

The same conditional convolution idea could be tested for point cloud attribute compression, such as color or normals, since the decoder already has a per-voxel conditioning channel and the paper does not report such experiments.
One testable extension is to reduce the number of routing weights per stage or quantize them coarsely; because they come only from pooled point features, it is plausible that most of the guidance survives heavy compression, though the paper does not study this.
The comparison against SparsePCGC mixes coding pipelines, so the reported gap should not be read as an intrinsic limit of interlaced point-voxel architectures; a like-for-like variant would be needed to know.
The pattern of parallel branches with cross-feeding at each scale may transfer to other sparse 3D tasks such as segmentation or scene completion, where one network must respect both global layout and local geometry; this is an analogy, not a result of the paper.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

You should know this about PVINet: it is a workmanlike architecture paper with a legitimate new arrangement. The point-voxel interlaced encoder runs a voxel branch and a point branch in parallel, exchanges features at each scale, and decodes with conditional sparse convolutions. The reported 15.5% and 8% BD-rate gains over PCGCv2 and DeepPCC are consistent across the 12 MPEG/JPEG CTC point clouds, and the paper is honest enough to report the clear loss to SparsePCGC (22.9% worse D1). So the abstract-level 'competitive' claim is defensible. What is genuinely useful: the parallel interlaced design is a real departure from the cascaded point-voxel baselines (GRASP-Net, PIVOT-Net, DeepPCC). The ablation shows the interlaced structure alone buys 10.3% BD-rate over a voxel-only model, which supports the core idea. The conditional sparse convolution is a reasonable adaptation of dynamic/conditional convolution to sparse 3D, and the authors correctly identify it as the main mechanism for point-to-voxel interaction. The soft spots are real but not disqualifying. The biggest one is exactly what the stress-test note flagged: the 7.7% gain attributed to conditional sparse convolution is not parameter-matched. Replacing a standard convolution with a mixture of n expert kernels adds capacity and FLOPs; without a control that matches parameter count, the gain could reflect size rather than input-dependent routing. Relatedly, the routing weights are computed from a global average pool of point features (R^{1xC}), so they are per-stage global descriptors, not per-voxel. The claim that these weights 'guide' fine local reconstruction is overstated; the decoder likely benefits more from the voxel features themselves. The paper also omits PIVOT-Net despite citing it, reports no error bars or repeated-seed results, and runs the ablation on only two sequences. The intro says 'superiority' over state-of-the-art, which is contradicted by the SparsePCGC numbers. No code or key hyperparameters (e.g., n, bit cost of routing weights) are given. All told, this is a contribution worth engaging with. It is an incremental step in learned point cloud geometry compression, not a paradigm shift. A reader building hybrid point-voxel codecs will want this on the stack. It deserves a serious referee: the architecture is clearly thought through, the evaluation is standard and mostly careful, and the main open question - whether conditional convolution is truly better than a larger standard convolution - is testable with a simple ablation. I would engage with it, but ask for a parameter-matched control and a PIVOT-Net comparison before accepting the central claim.

Referee Report

4 major / 5 minor

Summary. The paper proposes PVINet, a hybrid point-voxel architecture for lossy point cloud geometry compression. A voxel-based encoder captures global structure while a parallel point-based encoder models local context; the two branches interact at multiple scales, with the point branch feeding the voxel branch through a proposed conditional sparse convolution whose kernels are assembled as a weighted mixture of expert kernels (Eq. 1). During decoding, the transmitted routing weights guide voxel-feature reconstruction. The method is evaluated on 12 MPEG/JPEG CTC point clouds against G-PCC, GRASP-Net, HPSR-PCGC, PCGCv2, DeepPCC, and SparsePCGC, reporting BD-rate gains over most baselines and an acknowledged loss to SparsePCGC. An ablation (Fig. 4) attributes gains to the interlaced structure (10.3% BD-rate) and to the conditional sparse convolution (7.7% BD-rate).

Significance. If the claimed results hold, PVINet offers a plausible architecture-level improvement over previous point-voxel hybrids by parallelizing rather than cascading the two representations and by using a lightweight side channel for conditional decoding. The paper has concrete strengths: evaluation on standard CTC test conditions, comparison with six baselines, a clear ablation isolating the interlaced structure, and honest acknowledgment of the SparsePCGC gap, which guards against overclaiming. The central novelty—conditional sparse convolution with transmitted routing weights—is interesting and could be useful beyond compression. However, the specific evidence for the conditional mechanism's contribution is not yet load-bearing because the ablation does not control for capacity or clarify the information content of the routing weights.

major comments (4)

[§III-C, Fig. 4] The 7.7% BD-rate gain attributed to conditional sparse convolution is not established as a property of input-dependent routing. The comparison 'interlaced_model' vs. PVINet changes the convolutional layer to a mixture of n expert kernels (Eq. 1), which increases parameter count and FLOPs. A standard sparse convolution with matched parameter count, or a non-conditional mixture with input-independent weights, is needed to separate capacity from conditioning. Without this control, the stated contribution is ambiguous.
[§II-A, Eq. (1); §II-B] The routing weights are computed from Pool(F_point_stage1) ∈ R^{1×C}, a single global descriptor per stage. Such a vector cannot convey per-voxel or per-region local geometry; therefore the claim in §II-C that the decoder uses routing weights 'as guidance' to recover fine local details is not supported at the architectural level. The paper should either use spatially localized routing or present evidence (e.g., visualization or a variant with random/absent routing weights) that the transmitted weights, rather than the voxel features alone, drive the decoder gain.
[§III-B, Table I] All BD-rate numbers are reported as point estimates with no repeated runs, error bars, or significance tests. The claimed gains over DeepPCC are small (−8.0% D1, −6.1% D2 on average, and near zero on several sequences such as Phil D1 +0.2%), so without variance estimates the headline '8% bit rate savings' is not statistically robust. Since training is stochastic and the models are trained for only 25 epochs, the authors should provide at least three seeds or a significance analysis for the main comparisons.
[Table I, SparsePCGC column] The SparsePCGC entries are corrupted in the manuscript (e.g., '25.5 20.23MUVB David', '20.32 19.36(9bit)'), making per-sequence losses unreadable. Only the average row is legible. These data need to be regenerated; as written, the table is not reproducible and prevents the reader from assessing the claimed SparsePCGC gap.

minor comments (5)

[§III-C] The ablation reports only aggregate BD-rate gains on two sequences; per-sequence BD-rate values should be listed so the reader can see whether the 10.3% and 7.7% gains are consistent or driven by a single point cloud.
[§I, §III-B] PIVOT-Net [18] is cited as a related hybrid framework but is not included in the experiments. Adding it (or explicitly stating why it is omitted) would strengthen the comparison, especially since PIVOT-Net also uses point-voxel-tree interactions.
[§III-C] The sentence 'while maintaining efficient inference complexity' is not supported by any runtime, parameter, or FLOP measurement. Either provide the complexity comparison or remove the claim.
[Fig. 1] The annotation 'Only used for training' is not explained in the text. It is unclear which components are disabled at test time and how that affects the encoding/decoding pipeline.
[Throughout] There are numerous typos and spacing errors ('V oxel', 'T able', 'Ovrerall', 'Exercixe', inconsistent comma placement). A careful proofread is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PVINet is an empirical architecture paper whose claimed gains are measured against external benchmarks, with no derivation reducing to its inputs.

full rationale

PVINet makes no formal derivation; its central claim is an empirical RD comparison. The interlaced point-voxel encoder, conditional sparse convolution (Eq. 1), and entropy coding are trained end-to-end on ShapeNet and evaluated on held-out MPEG/JPEG CTC point clouds against external baselines (PCGCv2, DeepPCC, SparsePCGC, etc.). The routing weights are part of the learned model and are transmitted as side information; they are not fitted to the test set and then reported as a prediction. The ablation in Sec. III-C compares the voxel-only model, the interlaced model, and the full PVINet; while this ablation does not explicitly control for parameter count, that is a methodological limitation, not a circularity pattern. There are no load-bearing self-citations, no invoked uniqueness theorem from the authors' prior work, and no equation in which an output is defined in terms of the quantity it is supposed to predict. The comparison with SparsePCGC is explicitly qualified by a difference in lossless-thumbnail multistage strategy, which is an honest limitation statement rather than a circular dodge. Overall, the paper's evidence is self-contained empirical benchmarking, so the circularity score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 2 invented entities

The ledger shows the central claim depends on several unreported hyperparameters (K, C, n, voxel scales) and on the untested assumption that low-bitrate routing weights can substitute for richer point features at the decoder. There is no heavy circularity, but the architecture rests on several hand-chosen design values.

free parameters (5)

Rate-distortion trade-off lambda = five values: 0.1, 0.5, 1, 2, 3
Standard RD control parameter; only five operating points are used for BD-rate, which can affect the computed gains.
Number of nearest neighbors K in Local Graph Construction = not reported
K appears explicitly in the LGC block (Section II-A) but no value is given; it controls the local context representation.
Number of expert kernels n in routing function = not reported
Equation (1) defines the kernel as a weighted sum of n expert kernels, but n is never specified.
Feature channel width C = not reported
All feature dimensions are written as C, but the actual channel counts at each stage are omitted.
Voxel resolution and downsampling factors at each of three scales = not reported
The encoder has three voxel scales, but the voxel sizes and pruning/downsampling rates are not stated.

assumptions (5)

standard math Additive uniform noise is a valid differentiable surrogate for quantization in rate-distortion training.
Invoked in Section II-D following Balle et al. [22]; it is a standard relaxation but not exact.
domain assumption Voxel occupancy cross-entropy correlates with the reported D1/D2 geometric distortion metrics.
Loss function Eq. (3) optimizes occupancy BCE, while evaluation uses point-to-point and point-to-plane PSNR; the proxy is standard in learned PCC but not exact.
ad hoc to paper Transmitting only routing weights, not full point embeddings, is sufficient to convey local geometry cues to the decoder.
Section II-B states point features are not transmitted and routing weights provide complementary cues; this is the key side-information assumption with no information-theoretic justification.
domain assumption Lossless G-PCC coding of downsampled coordinates does not distort cross-method rate comparisons.
Section III-A excludes downsampled coordinates from training and codes them losslessly with G-PCC; SparsePCGC uses a different multistage strategy, making that comparison non-apples-to-apples.
domain assumption Models trained on ShapeNet generalize to the 8iVFB, Owlii, and MVUB evaluation clouds.
Training data and evaluation data differ in scale and content; this is standard in PCC papers but is an assumption about generalization.

invented entities (2)

Conditional sparse convolution with softmax routing over expert kernels
purpose: Dynamically customize sparse convolution kernels using point-derived features so the voxel branch adapts to local context.
Introduced and evaluated only in this paper; no external theoretical or empirical handle. The mechanism closely resembles dynamic/conditional convolution from prior work, which is not cited.
Routing weights as transmitted side information
purpose: Carry local-context cues from the point encoder to the decoder at negligible bitrate.
The paper asserts 'negligible coding bits' and 'complementary cues' but provides no rate allocation analysis or information-content measurement.

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Cite this review

Pith. "Pith review of PVINet: Point-Voxel Interlaced Network for Point Cloud Compression." pith.science (2026). https://pith.science/paper/LDPRF75A

@misc{pith2026250901097,
  author       = {Pith},
  title        = {Pith review of: PVINet: Point-Voxel Interlaced Network for Point Cloud Compression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LDPRF75A}},
  note         = {Machine review of arXiv:2509.01097}
}

read the original abstract

In point cloud compression, the quality of a reconstructed point cloud relies on both the global structure and the local context, with existing methods usually processing global and local information sequentially and lacking communication between these two types of information. In this paper, we propose a point-voxel interlaced network (PVINet), which captures global structural features and local contextual features in parallel and performs interactions at each scale to enhance feature perception efficiency. Specifically, PVINet contains a voxel-based encoder (Ev) for extracting global structural features and a point-based encoder (Ep) that models local contexts centered at each voxel. Particularly, a novel conditional sparse convolution is introduced, which applies point embeddings to dynamically customize kernels for voxel feature extraction, facilitating feature interactions from Ep to Ev. During decoding, a voxel-based decoder employs conditional sparse convolutions to incorporate point embeddings as guidance to reconstruct the point cloud. Experiments on benchmark datasets show that PVINet delivers competitive performance compared to state-of-the-art methods.

Figures

Figures reproduced from arXiv: 2509.01097 by the authors.

**Figure 1.** Overview of the PVINet. The framework consists of three key modules: a point-voxel interlaced encoder with parallel voxel-based encoder and [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** An illustration of the conditional sparse convolution, where different [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 5.** Visualization of geometric reconstruction results on David9. The final [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

28 extracted references · 26 canonical work pages

[1]

Emerging mpeg standards for point cloud compression,

S. Schwarz, M. Preda, V . Baroncini, M. Budagavi, P. Cesar, P. A. Chou, R. A. Cohen, M. Krivoku ´ca, S. Lasserre, Z. Li et al. , “Emerging mpeg standards for point cloud compression,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems , vol. 9, no. 1, pp. 133–148, 2018

work page 2018
[2]

Octree-based point-cloud compression

R. Schnabel and R. Klein, “Octree-based point-cloud compression.” PBG@ SIGGRAPH , vol. 3, pp. 111–121, 2006

work page 2006
[3]

Patch-based deep autoencoder for point cloud geometry compression,

K. You and P. Gao, “Patch-based deep autoencoder for point cloud geometry compression,” in Proceedings of the 3rd ACM International Conference on Multimedia in Asia , 2021, pp. 1–7

work page 2021
[4]

Ipdae: Improved patch-based deep autoen- coder for lossy point cloud geometry compression,

K. You, P. Gao, and Q. Li, “Ipdae: Improved patch-based deep autoen- coder for lossy point cloud geometry compression,” inProceedings of the 1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis , 2022, pp. 1–10

work page 2022
[5]

Deep compression for dense point cloud maps,

L. Wiesmann, A. Milioto, X. Chen, C. Stachniss, and J. Behley, “Deep compression for dense point cloud maps,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2060–2067, 2021

work page
[6]

Kpconv: Flexible and deformable convolution for point clouds,

H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, “Kpconv: Flexible and deformable convolution for point clouds,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 6411–6420

work page 2019
[7]

Density- preserving deep point cloud compression,

Y . He, X. Ren, D. Tang, Y . Zhang, X. Xue, and Y . Fu, “Density- preserving deep point cloud compression,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2333–2342

work page 2022
[8]

3d point cloud attribute compression via graph prediction,

S. Gu, J. Hou, H. Zeng, and H. Yuan, “3d point cloud attribute compression via graph prediction,” IEEE Signal Processing Letters , vol. 27, pp. 176–180, 2020

work page 2020

Show all 28 references

[9]

Hierarchical prior-based super resolution for point cloud geometry compression,

D. Li, K. Ma, J. Wang, and G. Li, “Hierarchical prior-based super resolution for point cloud geometry compression,” IEEE Transactions on Image Processing , 2024

2024
[10]

Learning convolutional trans- forms for lossy point cloud geometry compression,

M. Quach, G. Valenzise, and F. Dufaux, “Learning convolutional trans- forms for lossy point cloud geometry compression,” in 2019 IEEE international conference on image processing (ICIP) . IEEE, 2019, pp. 4320–4324

2019
[11]

Multiscale point cloud geometry compression,

J. Wang, D. Ding, Z. Li, and Z. Ma, “Multiscale point cloud geometry compression,” in 2021 Data Compression Conference (DCC) . IEEE, 2021, pp. 73–82

2021
[12]

Sparse tensor- based multiscale representation for point cloud geometry compres- sion,

J. Wang, D. Ding, Z. Li, X. Feng, C. Cao, and Z. Ma, “Sparse tensor- based multiscale representation for point cloud geometry compres- sion,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 7, pp. 9055–9071, 2022

2022
[13]

Geometric prior based deep human point cloud geometry compression,

X. Wu, P. Zhang, M. Wang, P. Chen, S. Wang, and S. Kwong, “Geometric prior based deep human point cloud geometry compression,” IEEE Transactions on Circuits and Systems for Video Technology , 2024

2024
[14]

Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction,

C. Sun, H. Yuan, X. Mao, X. Lu, and R. Hamzaoui, “Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction,” IEEE Signal Processing Letters, 2024

2024
[15]

Distance-based probability model for octree coding,

R. L. de Queiroz, D. C. Garcia, P. A. Chou, and D. A. Florencio, “Distance-based probability model for octree coding,” IEEE Signal Processing Letters, vol. 25, no. 6, pp. 739–742, 2018

2018
[16]

Set partitioning in hierarchical trees for point cloud attribute compression,

A. L. Souto, V . F. Figueiredo, P. A. Chou, and R. L. de Queiroz, “Set partitioning in hierarchical trees for point cloud attribute compression,” IEEE Signal Processing Letters , vol. 28, pp. 1903–1907, 2021

1903
[17]

Grasp-net: Geometric residual anal- ysis and synthesis for point cloud compression,

J. Pang, M. A. Lodhi, and D. Tian, “Grasp-net: Geometric residual anal- ysis and synthesis for point cloud compression,” in Proceedings of the 1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis , 2022, pp. 11–19

2022
[18]

Pivot-net: Heterogeneous point-voxel-tree- based framework for point cloud compression,

J. Pang, K. Bui, and D. Tian, “Pivot-net: Heterogeneous point-voxel-tree- based framework for point cloud compression,” in 2024 International Conference on 3D Vision (3DV) . IEEE, 2024, pp. 1270–1279

2024
[19]

Deeppcc: Learned lossy point cloud compression,

J. Zhang, G. Liu, J. Zhang, D. Ding, and Z. Ma, “Deeppcc: Learned lossy point cloud compression,” IEEE Transactions on Emerging Topics in Computational Intelligence , 2024

2024
[20]

Pointconv: Deep convolutional networks on 3d point clouds,

W. Wu, Z. Qi, and L. Fuxin, “Pointconv: Deep convolutional networks on 3d point clouds,” in Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , 2019, pp. 9621–9630

2019
[21]

Unsupervised degradation representation learning for unpaired restoration of images and point clouds,

W. Longguang, G. Yulan, W. Yingqian, D. Xiaoyu, Y . Qingyu, Xuand Jungang, and A. Wei, “Unsupervised degradation representation learning for unpaired restoration of images and point clouds,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024

2024
[22]

Vari- ational image compression with a scale hyperprior,

J. Ball ´e, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Vari- ational image compression with a scale hyperprior,” arXiv preprint arXiv:1802.01436, 2018

2018 arXiv
[23]

Shapenet: An information- rich 3d model repository,

A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su et al., “Shapenet: An information- rich 3d model repository,” arXiv preprint arXiv:1512.03012 , 2015

2015 arXiv
[24]

8i voxelized full bodies-a voxelized point cloud dataset,

E. d’Eon, B. Harrison, T. Myers, and P. A. Chou, “8i voxelized full bodies-a voxelized point cloud dataset,” ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG) input document WG11M40059/WG1M74006, vol. 7, no. 8, p. 11, 2017

2017
[25]

Owlii dynamic human mesh sequence dataset. iso,

Y . Xu, Y . Lu, and Z. Wen, “Owlii dynamic human mesh sequence dataset. iso,” IEC JTC1/SC29/WG11 m41658, Tech. Rep., 2017

2017
[26]

Microsoft voxelized upper bodies-a voxelized point cloud dataset,

C. Loop, Q. Cai, S. O. Escolano, and P. A. Chou, “Microsoft voxelized upper bodies-a voxelized point cloud dataset,” ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG) input document m38673 M , vol. 72012, p. 2016, 2016

2016
[27]

Com- mon test conditions for point cloud compression,

S. Schwarz, G. Martin-Cocher, D. Flynn, and M. Budagavi, “Com- mon test conditions for point cloud compression,” Document ISO/IEC JTC1/SC29/WG11 w17766, Ljubljana, Slovenia , 2018

2018
[28]

Jpeg pleno point cloud coding common test conditions,

J. P. PCC, “Jpeg pleno point cloud coding common test conditions,” JPEG (ISO/IEC JTC 1/SC 29/WG1) , 2019

2019

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Emerging mpeg standards for point cloud compression,

S. Schwarz, M. Preda, V . Baroncini, M. Budagavi, P. Cesar, P. A. Chou, R. A. Cohen, M. Krivoku ´ca, S. Lasserre, Z. Li et al. , “Emerging mpeg standards for point cloud compression,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems , vol. 9, no. 1, pp. 133–148, 2018

work page 2018

[2] [2]

Octree-based point-cloud compression

R. Schnabel and R. Klein, “Octree-based point-cloud compression.” PBG@ SIGGRAPH , vol. 3, pp. 111–121, 2006

work page 2006

[3] [3]

Patch-based deep autoencoder for point cloud geometry compression,

K. You and P. Gao, “Patch-based deep autoencoder for point cloud geometry compression,” in Proceedings of the 3rd ACM International Conference on Multimedia in Asia , 2021, pp. 1–7

work page 2021

[4] [4]

Ipdae: Improved patch-based deep autoen- coder for lossy point cloud geometry compression,

K. You, P. Gao, and Q. Li, “Ipdae: Improved patch-based deep autoen- coder for lossy point cloud geometry compression,” inProceedings of the 1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis , 2022, pp. 1–10

work page 2022

[5] [5]

Deep compression for dense point cloud maps,

L. Wiesmann, A. Milioto, X. Chen, C. Stachniss, and J. Behley, “Deep compression for dense point cloud maps,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2060–2067, 2021

work page

[6] [6]

Kpconv: Flexible and deformable convolution for point clouds,

H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, “Kpconv: Flexible and deformable convolution for point clouds,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 6411–6420

work page 2019

[7] [7]

Density- preserving deep point cloud compression,

Y . He, X. Ren, D. Tang, Y . Zhang, X. Xue, and Y . Fu, “Density- preserving deep point cloud compression,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2333–2342

work page 2022

[8] [8]

3d point cloud attribute compression via graph prediction,

S. Gu, J. Hou, H. Zeng, and H. Yuan, “3d point cloud attribute compression via graph prediction,” IEEE Signal Processing Letters , vol. 27, pp. 176–180, 2020

work page 2020