Pith. sign in

REVIEW 3 major objections 4 minor 33 references

BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper introduces BVI-CR, an 18-sequence multi-view RGB-D human dataset with textured meshes, and benchmarks three codecs under MPEG MIV conditions, finding that the neural codec MV-HiNeRV cuts bitrate by an average 38.5% in PSNR…

desk verdict A genuinely new multi-view RGB-D dataset that deserves a serious referee, but the benchmark claims need more detail and the data must actually be released before the contribution can be verified. read the letter →

arxiv 2411.11199 v1 pith:RICI2S6X submitted 2024-11-17 cs.CV eess.IV

Ge Gao , Adrian Azzarelli , Ho Man Kwan , Nantheera Anantrasirichai , Fan Zhang , Oliver Moolan-Feroze , David Bull This is my paper · ORCID
classification cs.CVeess.IV
keywords volumetricvideomulti-viewRGB-DdatasettexturedmeshimplicitneuralrepresentationcompressionBD-rateimmersivefree-viewpoint
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 presents BVI-CR, a public dataset of 18 multi-view RGB-D human sequences with corresponding textured polygonal meshes, designed to support volumetric video compression research. Using this dataset, the authors benchmark three codecs under MPEG MIV Common Test Conditions: the MPEG test model TMIV and two implicit neural representation (INR) codecs, MV-HiNeRV and MV-IERV. The central result is that MV-HiNeRV achieves the best rate-distortion performance across all metrics and sequences, with an average 38.5% BD-rate reduction in PSNR and 34.3% in IV-PSNR relative to TMIV, while MV-IERV also outperforms TMIV by 29.5% and 26.9% respectively. If this holds, the dataset offers a credible public platform for developing and comparing volumetric video codecs, and strengthens the case for INR-based compression as a practical alternative to conventional multi-view coding.

What carries the argument

The central object is the BVI-CR dataset itself, built from 10 synchronized Azure RGB-D cameras arranged on a hemisphere around a 2.5m capture area, with volumes reconstructed via TSDF fusion with non-rigid registration, meshes extracted by marching cubes, and textures generated through triangle-based UV mapping. The benchmark machinery is the MPEG MIV common test condition: all ten source views are encoded and decoded, four additional front-facing synthesized views are rendered by the TMIV default view synthesizer, and rate-distortion performance is compared using PSNR, SSIM, IV-PSNR, and Bjøntegaard $\Delta$ Rate (BD-rate) relative to the TMIV anchor. The two INR codecs, MV-HiNeRV and MV-IERV, are the competing neural representations whose reconstruction quality is evaluated against these metrics.

What would settle it

A reader could take a few BVI-CR sequences, compare the provided meshes to a high-precision independent 3D scan of the same actor and pose (e.g., a structured-light or laser scan), and compute per-vertex or surface-to-surface error; if the reconstruction error is comparable to or larger than the compression quality differences between the codecs, the benchmark conclusions would be undermined.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that the newly released BVI-CR dataset, comprising 18 diverse multi-view RGB-D captures with corresponding textured meshes, enables a fair benchmark of volumetric video codecs, and under this benchmark the INR-based codec MV-HiNeRV outperforms the MPEG MIV anchor TMIV by an average of 38.5% in BD-rate measured by PSNR and 34.3% measured by IV-PSNR, with MV-IERV also showing large gains of 29.5% and 26.9%. The claim is that neural, coordinate-based representations are a promising and competitive alternative to conventional multi-view coding on this data.

Load-bearing premise

The benchmark and dataset utility rest on the assumption that the TSDF fusion with non-rigid registration followed by marching cubes and UV mapping produces accurate ground-truth geometry and texture for all 18 sequences, and no independent quantitative reconstruction accuracy check is reported.

Editorial extensions

If this is right

  • A public dataset of 18 sequences lets researchers train and validate volumetric compression and reconstruction methods without needing to build their own multi-camera capture rig.
  • The reported BD-rate reductions suggest that INR-based codecs can deliver the same quality at roughly 38% lower bitrate than TMIV on this content, pointing toward practical neural volumetric video delivery.
  • The inclusion of multi-person interactions and occluded actions provides stress cases where the relative gains of INR codecs are smaller, indicating where further codec improvements are needed.
  • The availability of camera extrinsics, depth maps, background masks, and textured meshes supports downstream tasks beyond compression, including quality assessment and neural rendering.

Reading between the lines

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

  • The reported gains may partly depend on the reconstruction pipeline's output being treated as ground truth; if those meshes were compared against an independent high-resolution 3D reference, the compression-quality conclusions could shift.
  • Because BD-rate is computed with TMIV as the single anchor, the results may not generalize to other conventional codecs or to end-to-end volumetric rendering quality; a natural extension is to add subjective quality assessment on rendered views.
  • The dataset could be extended with audio tracks, as the authors plan, enabling joint audiovisual volumetric compression research.
  • A testable extension is to use BVI-CR to pretrain generalizable neural codecs across sequences and measure cross-content generalization, which would indicate how well the reported gains transfer to unseen captures.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper introduces BVI-CR, a multi-view volumetric human dataset consisting of 18 sequences (15 single-person, 3 multi-person) captured with 10 synchronized RGB-D cameras, together with background masks and textured polygonal meshes generated by a TSDF fusion and non-rigid registration pipeline. The authors also benchmark one conventional codec (TMIV) and two implicit-neural-representation (INR) codecs (MV-HiNeRV and MV-IERV) on this dataset, reporting BD-rate savings that reach 38.5% for MV-HiNeRV relative to the TMIV anchor. The paper positions BVI-CR as an open resource for volumetric video compression, reconstruction, and quality assessment.

Significance. If the dataset is released as described and the benchmark is fair, BVI-CR would fill a real gap: existing volumetric human datasets either lack multi-view depth, lack textured meshes, have limited multi-person content, or are not oriented toward compression evaluation. The inclusion of 10 synchronized RGB-D views plus meshes and masks, with diverse actions and occlusions, is genuinely useful. The benchmark also provides evidence that INR-based codecs can substantially outperform the MPEG test model on multi-view volumetric video, which is a significant and timely result. The paper's explicit parallelization with MIV CTC is a strength, as is the use of multiple metrics including IV-PSNR. However, the current manuscript does not yet make the dataset accessible or verifiable, and the benchmark protocol is insufficiently specified for the headline claim to be fully trusted.

major comments (3)
  1. [Section III, Table II] The central claim that MV-HiNeRV achieves a 38.5% average BD-rate reduction over TMIV depends on a symmetric rate-distortion comparison, but the paper does not specify how depth is handled in the rendering and how INR bitrate is counted. The text states that synthesized views are rendered 'given the decoded texture attributes (of ten source views)' but does not say whether the depth maps input to the TMIV view synthesizer are the decoded depth produced by each codec or the original uncompressed depth. Since TMIV encodes both texture and depth with VVenC, any asymmetry here would directly inflate or deflate the reported gains. In addition, the bitrate definition for MV-HiNeRV and MV-IERV is not given: the reader cannot tell whether model parameters, latent codes, and background masks are included, and whether bpp is computed over the full frame or only the foreground region. Please provide the exact rendering input per codec and a complete, equation-level definition of bitrate for all three codecs.
  2. [Section II-A, Section II-B, Table I] The paper contains internally inconsistent capture specifications that affect both the dataset documentation and the benchmark calculations. Section II-A states that the cameras capture 2560x1440 RGB at 30 FPS and that the ToF depth sensor produces 640x576 depth images at the same frame rate, while Section II-B and Table I report 25 FPS and 512x424 depth resolution, and the abstract says 30 FPS. These discrepancies propagate to the stated frame counts (300-450 frames) and to any bitrate or BD-rate computation. The authors should clarify the native capture resolution and frame rate, and describe any resampling or cropping applied before releasing the RGB and depth data.
  3. [Section II, dataset availability and validation] The central deliverable of the paper is the dataset itself, but the provided link (https://github.com/fan-aaron-zhang/bvi-cr) is not shown to contain any data or code, and no benchmark code is released. This makes it impossible for a reader to verify the dataset's content, format, or the reproducibility of the reported benchmark. Moreover, the volumetric reconstruction pipeline (TSDF fusion, non-rigid registration, marching cubes, UV mapping) is described only qualitatively, with no quantitative measure of reconstruction accuracy against an independent reference. Since all downstream compression results inherit the geometry and texture quality of these meshes, the paper should include either a data release with actual download instructions, dataset statistics and validation metrics, or an explicit, measured discussion of reconstruction error and its limitations.
minor comments (4)
  1. [Introduction] There are several typographical errors in the text, including 'for for' in the second paragraph, 'mutli-camera' in Section II-A, 'a a set' in Section II-A, and 'persumably' in Section III; these should be corrected.
  2. [Figure 4 and Figure 5] The RD curves in Figure 4 and the qualitative comparison in Figure 5 do not identify which sequence or sequences they correspond to; since the paper reports per-sequence variation, please state the sequence index in the captions.
  3. [Section III] The statement that the benchmark 'follow[s] the MPEG MIV Common Test Conditions' is vague; the paper does not specify the quantization parameter ladders, rate points, or the exact version of TMIV and VVenC used, which are needed to reproduce the results.
  4. [Section II-A] The description of the depth-to-RGB alignment is incomplete: the paper lists different resolutions for RGB and depth, but does not explain how they are registered or whether the released depth maps are upsampled to the RGB resolution or kept native.

Circularity Check

0 steps flagged · score 2.0 of 10

No construction-level circularity: the dataset is newly captured and the benchmark results are measured on it; the only self-citation (MV-HiNeRV's source paper) is not load-bearing.

full rationale

The paper's central deliverable is the BVI-CR dataset, 18 newly captured multi-view RGB-D sequences with meshes, which is independent of the authors' previous work. Its reconstruction pipeline cites external, peer-reviewed algorithms (DynamicFusion [28] and marching cubes [29]), and no equation or definition in the paper makes the dataset equivalent to the benchmark outcomes or vice versa. The compression benchmark in Section III is an empirical comparison performed on this new dataset: TMIV, MV-IERV, and MV-HiNeRV are optimized according to their original papers, and all rate-distortion points, BD-rates, and qualitative views in Table II and Figures 4-5 are generated in this paper rather than imported from prior publications. The only notable self-citation is reference [16] for MV-HiNeRV, whose authors overlap with the present paper; this is a mild conflict-of-interest signal for the favorable comparison, but the benchmark does not reduce by construction to any fitted parameter, defined quantity, or prior result from [16], because the dataset is new and the RD curves are measured here. The internal inconsistencies noted in the manuscript (depth resolution 640x576 in Section II-A vs 512x424 in Section II-B; 30 FPS in the abstract vs 25 FPS in Table I; unspecified depth and bitrate accounting for the INR codecs) are correctness, reproducibility, and benchmark-symmetry concerns, not circularity: they do not make any claim definitionally equal to its input. Since no specific reduction can be exhibited, the circularity score is low.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities or mathematical objects. Its central claim rests on the accuracy of the reconstruction pipeline, the quality of the learned masks, and the unspecified rate points used for BD-rate computation; these are the main unverified premises.

free parameters (2)
  • TSDF voxel resolution and fusion parameters = Not reported
    The mesh quality, and therefore every compression benchmark built on the meshes, depends on the TSDF grid resolution and non-rigid registration settings chosen in the capture pipeline of Section II-A.
  • Benchmark rate points and QP ladders = Not reported
    The claimed BD-rate gains of 38.5% depend on where each codec's rate-distortion curve is sampled. Section III says the MIV Common Test Conditions were followed, but the actual rate points and QP values are not given.
assumptions (3)
  • domain assumption The DynamicFusion-based TSDF fusion and non-rigid registration pipeline produces geometrically accurate meshes and textures for all 18 sequences.
    Section II-A/B describes the reconstruction process but provides no quantitative validation against an independent 3D reference; mesh accuracy is assumed from the capture and fusion design.
  • domain assumption The FCN-ResNet50 background masks are accurate enough to support compression evaluation.
    Section II-B states the masks were 'empirically found to give the most consistent performance' but reports no mask quality metric or comparison against manual segmentation.
  • domain assumption The 18 selected sequences are representative of the challenges targeted by volumetric video compression.
    The authors designed actions to include occlusions and fast motion, but no coverage analysis or comparison to a broader action distribution is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression." pith.science (2026). https://pith.science/paper/RICI2S6X

@misc{pith2026241111199,
  author       = {Pith},
  title        = {Pith review of: BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RICI2S6X}},
  note         = {Machine review of arXiv:2411.11199}
}
read the original abstract

The advances in immersive technologies and 3D reconstruction have enabled the creation of digital replicas of real-world objects and environments with fine details. These processes generate vast amounts of 3D data, requiring more efficient compression methods to satisfy the memory and bandwidth constraints associated with data storage and transmission. However, the development and validation of efficient 3D data compression methods are constrained by the lack of comprehensive and high-quality volumetric video datasets, which typically require much more effort to acquire and consume increased resources compared to 2D image and video databases. To bridge this gap, we present an open multi-view volumetric human dataset, denoted BVI-CR, which contains 18 multi-view RGB-D captures and their corresponding textured polygonal meshes, depicting a range of diverse human actions. Each video sequence contains 10 views in 1080p resolution with durations between 10-15 seconds at 30FPS. Using BVI-CR, we benchmarked three conventional and neural coordinate-based multi-view video compression methods, following the MPEG MIV Common Test Conditions, and reported their rate quality performance based on various quality metrics. The results show the great potential of neural representation based methods in volumetric video compression compared to conventional video coding methods (with an up to 38\% average coding gain in PSNR). This dataset provides a development and validation platform for a variety of tasks including volumetric reconstruction, compression, and quality assessment. The database will be shared publicly at \url{https://github.com/fan-aaron-zhang/bvi-cr}.

Figures

Figures reproduced from arXiv: 2411.11199 by the authors.

Figure 1
Figure 1. Snapshots of exemplary scenes from the BVI-CR dataset, comprising [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. (Left) the Metaverse Studio used for content capture. (Right) Illustration of the camera arrangement from two different angles. including the corresponding depth maps, background masks and textured meshes generated based on their multi-view content. Sample frames of a multi-view sequence in this database are shown in [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Selected pose examples with the sequence name, sequence index, and descriptions to the complete actions performed. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Rate-distortion performance comparison of selected baselines, with distortion measured by PSNR, SSIM, and IV-PSNR, respectively. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparison of synthesized views (cropped) for 3 randomly [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

33 extracted references · 23 canonical work pages

  1. [1]

    Concert experiences in virtual reality environments,

    K. E. Onderdijk, L. Bouckaert, E. Van Dyck, and P.-J. Maes, “Concert experiences in virtual reality environments,” Virtual Reality, vol. 27, no. 3, pp. 2383–2396, 2023

    work page 2023

  2. [2]

    From video streaming to virtual reality worlds: an academic, reflective, and creative study on live theatre and performance in the metaverse,

    A. Ba ´ıa Reis and M. Ashmore, “From video streaming to virtual reality worlds: an academic, reflective, and creative study on live theatre and performance in the metaverse,” International Journal of Performance Arts and Digital Media , vol. 18, no. 1, pp. 7–28, 2022

    work page 2022

  3. [3]

    Virtual reality conferencing: Multi-user immersive vr experiences on the web,

    S. N. Gunkel, H. M. Stokking, M. J. Prins, N. Van Der Stap, F. B. t. Haar, and O. A. Niamut, “Virtual reality conferencing: Multi-user immersive vr experiences on the web,” in Proceed- ings of the 9th ACM Multimedia Systems Conference , 2018, pp. 498–501

    work page 2018

  4. [4]

    Expe- riencing immersive virtual reality in museums,

    H. Lee, T. H. Jung, M. C. tom Dieck, and N. Chung, “Expe- riencing immersive virtual reality in museums,” Information & Management, vol. 57, no. 5, p. 103229, 2020

    work page 2020

  5. [5]

    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

  6. [6]

    Overview of the multiview high efficiency video coding (mv-hevc) standard,

    M. M. Hannuksela, Y . Yan, X. Huang, and H. Li, “Overview of the multiview high efficiency video coding (mv-hevc) standard,” in 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015, pp. 2154–2158

    work page 2015

  7. [7]

    Mpeg immersive video coding standard,

    J. M. Boyce, R. Dor ´e, A. Dziembowski, J. Fleureau, J. Jung, B. Kroon, B. Salahieh, V . K. M. Vadakital, and L. Yu, “Mpeg immersive video coding standard,” Proceedings of the IEEE , vol. 109, no. 9, pp. 1521–1536, 2021

    work page 2021

  8. [8]

    Nerf: Representing scenes as neural radiance fields for view synthesis,

    B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM, vol. 65, no. 1, pp. 99–106, 2021

    2021

Show all 33 references
  1. [9]

    Neural fields in visual computing and beyond,

    Y . Xie, T. Takikawa, S. Saito, O. Litany, S. Yan, N. Khan, F. Tombari, J. Tompkin, V . Sitzmann, and S. Sridhar, “Neural fields in visual computing and beyond,” in Computer Graphics Forum, vol. 41, no. 2. Wiley Online Library, 2022, pp. 641– 676

    2022

  2. [10]

    D-nerf: Neural radiance fields for dynamic scenes,

    A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, “D-nerf: Neural radiance fields for dynamic scenes,” in Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10 318–10 327

    2021

  3. [11]

    Waveplanes: A compact wavelet representation for dynamic neural radiance fields,

    A. Azzarelli, N. Anantrasirichai, and D. R. Bull, “Waveplanes: A compact wavelet representation for dynamic neural radiance fields,” arXiv preprint arXiv:2312.02218 , 2023

    2023 arXiv

  4. [12]

    Pnvc: To- wards practical inr-based video compression,

    G. Gao, H. M. Kwan, F. Zhang, and D. Bull, “Pnvc: To- wards practical inr-based video compression,” arXiv preprint arXiv:2409.00953, 2024

    2024 arXiv

  5. [13]

    Nvrc: Neural video representation compression,

    H. M. Kwan, G. Gao, F. Zhang, A. Gower, and D. Bull, “Nvrc: Neural video representation compression,” arXiv preprint arXiv:2409.07414, 2024

    2024

  6. [14]

    Implicit-explicit integrated representations for multi-view video compression,

    C. Zhu, G. Lu, B. He, R. Xie, and L. Song, “Implicit-explicit integrated representations for multi-view video compression,” arXiv preprint arXiv:2311.17350 , 2023

    2023 arXiv

  7. [15]

    Efficient dynamic-nerf based volumetric video coding with rate distortion optimization,

    Z. Zhang, G. Lu, H. Liang, A. Tang, Q. Hu, and L. Song, “Efficient dynamic-nerf based volumetric video coding with rate distortion optimization,” arXiv preprint arXiv:2402.01380 , 2024

    2024 arXiv

  8. [16]

    Immersive video compression using implicit neural representations,

    H. M. Kwan, F. Zhang, A. Gower, and D. Bull, “Immersive video compression using implicit neural representations,” in Picture Coding Symposium , 2024

    2024

  9. [17]

    Owlii dynamic human textured mesh sequence dataset,

    Y . Xu, Y . Lu, and Z. Wen, “Owlii dynamic human textured mesh sequence dataset,” in ISO/IEC JTC1/SC29/WG1 1 input document m41658, 2017

    2017

  10. [18]

    Hu- man3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments,

    C. Ionescu, D. Papava, V . Olaru, and C. Sminchisescu, “Hu- man3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments,” IEEE transactions on pattern analysis and machine intelligence , vol. 36, no. 7, pp. 1325–1339, 2013

    2013

  11. [19]

    Panoptic studio: A massively multiview system for social interaction capture,

    H. Joo, T. Simon, X. Li, H. Liu, L. Tan, L. Gui, S. Banerjee, T. S. Godisart, B. Nabbe, I. Matthews, T. Kanade, S. Nobuhara, and Y . Sheikh, “Panoptic studio: A massively multiview system for social interaction capture,” IEEE Transactions on Pattern Analysis and Machine Intell...

    2017

  12. [20]

    Humbi: A large multiview dataset of human body expressions,

    Z. Yu, J. S. Yoon, I. K. Lee, P. Venkatesh, J. Park, J. Yu, and H. S. Park, “Humbi: A large multiview dataset of human body expressions,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020

    2020

  13. [21]

    Real-time deep dynamic characters,

    M. Habermann, L. Liu, W. Xu, M. Zollhoefer, G. Pons-Moll, and C. Theobalt, “Real-time deep dynamic characters,” ACM Transactions on Graphics, vol. 40, no. 4, aug 2021

    2021

  14. [22]

    Uvg-vpc: V oxelized point cloud dataset for visual volumetric video-based coding,

    G. Gautier, A. Mercat, L. Fr ´eneau, M. Pitk ¨anen, and J. Vanne, “Uvg-vpc: V oxelized point cloud dataset for visual volumetric video-based coding,” in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX) . IEEE, 2023, pp. 244–247

    2023

  15. [23]

    Textured mesh vs coloured point cloud: A subjective study for volumetric video compression,

    E. Zerman, C. Ozcinar, P. Gao, and A. Smolic, “Textured mesh vs coloured point cloud: A subjective study for volumetric video compression,” in Twelfth International Conference on Quality of Multimedia Experience (QoMEX) , 2020

    2020

  16. [24]

    Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans,

    S. Peng, Y . Zhang, Y . Xu, Q. Wang, Q. Shuai, H. Bao, and X. Zhou, “Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans,” in CVPR, 2021

    2021

  17. [25]

    Learn to dance with aist++: Music conditioned 3d dance generation,

    R. Li, S. Yang, D. A. Ross, and A. Kanazawa, “Learn to dance with aist++: Music conditioned 3d dance generation,” 2021

    2021

  18. [26]

    Generalizable neural performer: Learning robust radi- ance fields for human novel view synthesis,

    W. Cheng, S. Xu, J. Piao, C. Qian, W. Wu, K.-Y . Lin, and H. Li, “Generalizable neural performer: Learning robust radi- ance fields for human novel view synthesis,” arXiv preprint arXiv:2204.11798, 2022

    2022 arXiv

  19. [27]

    Deepcloth: Neural garment representation for shape and style editing,

    Z. Su, T. Yu, Y . Wang, and Y . Liu, “Deepcloth: Neural garment representation for shape and style editing,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 2, pp. 1581–1593, 2023

    2023

  20. [28]

    Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time,

    R. A. Newcombe, D. Fox, and S. M. Seitz, “Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 343–352

    2015

  21. [29]

    Marching cubes: A high reso- lution 3d surface construction algorithm,

    W. E. Lorensen and H. E. Cline, “Marching cubes: A high reso- lution 3d surface construction algorithm,” in Seminal graphics: pioneering efforts that shaped the field , 1998, pp. 347–353

    1998

  22. [30]

    Fully convolutional networks for semantic segmentation,

    J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440

    2015

  23. [31]

    Vvenc: An open and optimized vvc encoder implemen- tation,

    A. Wieckowski, J. Brandenburg, T. Hinz, C. Bartnik, V . George, G. Hege, C. Helmrich, A. Henkel, C. Lehmann, C. Stoffers et al., “Vvenc: An open and optimized vvc encoder implemen- tation,” in 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) . IEEE, 20...

    2021

  24. [32]

    Iv-psnr—the objective quality metric for immersive video applications,

    A. Dziembowski, D. Mieloch, J. Stankowski, and A. Grzelka, “Iv-psnr—the objective quality metric for immersive video applications,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 11, pp. 7575–7591, 2022

    2022

  25. [33]

    Calculation of average psnr differences be- tween rd-curves,

    G. Bjontegaard, “Calculation of average psnr differences be- tween rd-curves,” ITU SG16 Doc. VCEG-M33 , 2001

    2001

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.