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Deformable Radial Kernel Splatting

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that a planar deformable radial kernel generalizes Gaussian splatting, letting a single primitive model sharp, non-elliptical shapes and cutting the primitive count by an order of magnitude while improving rendering…

desk verdict A genuinely more expressive splatting kernel that delivers real gains on clean synthetic data, but the unqualified SOTA claim does not survive the paper's own pose-noise experiments. read the letter →

arxiv 2412.11752 v2 pith:HWRRAHYT submitted 2024-12-16 cs.CV cs.GR

Yi-Hua Huang , Ming-Xian Lin , Yang-Tian Sun , Ziyi Yang , Xiaoyang Lyu , Yan-Pei Cao , Xiaojuan Qi This is my paper · ORCID
classification cs.CVcs.GR
keywords 3DGaussiansplattingdeformableradialkernelnovelviewsynthesisradiancefieldsdifferentiablerenderingshapeprimitivescullingposerobustness
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 tries to establish that the Gaussian kernel is not the best primitive for radiance-field splatting, and that a planar 'deformable radial kernel' — a shape built from learnable polar radii, angles, a hybrid L1/L2 distance, and a piecewise edge-sharpening map — can represent sharp-edged, non-elliptical scene content with far fewer primitives and better rendering quality. If right, it matters because 3D Gaussian splatting is a dominant real-time rendering method, and DRK offers a drop-in replacement that inherits its pipeline while removing its smoothness bottleneck. The paper reports state-of-the-art PSNR, LPIPS, and SSIM on its new benchmark covering textures, geometry, specular effects, and large scenes, and on a standard real-world unbounded benchmark, with primitive counts reduced by up to an order of magnitude at the sparsest setting. It also shows that the advantage depends on accurate camera poses.

What carries the argument

The load-bearing object is the deformable radial kernel: a 2D planar primitive parameterized by center $\mu$, rotation $q$, K radial lengths $s_k$ and polar angles $\theta_k$, a curvature blend weight $\eta \in (0,1)$, a sharpness parameter $\tau \in (-1,1)$, opacity $o$, and spherical harmonics. The kernel function interpolates between adjacent radial bases using cosine weights in the angular coordinate, which is exactly the mechanism that makes a 2D Gaussian a special case; blending in the L1 norm via $\eta$ gives straight edges, and the piecewise linear map $\Psi$ decouples edge sharpness from spatial scale. Around this kernel, the paper builds a rasterizer with polygon-based tile culling from the radial endpoints, an 8-entry cache-sorted depth ordering based on ray-plane intersection distance, and a view-dependent low-pass filter adapted from surface splatting.

What would settle it

A decisive test: on a sharp-edged scene with accurately estimated poses and equal primitive counts, DRK must beat Gaussian splatting on PSNR and LPIPS; under $10^{-3}$ pose noise the paper's own table predicts DRK falls below Gaussian splatting, and observing otherwise would overturn its robustness limitation.

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Extended reading notes

Core claim

The central claim is that replacing the radially symmetric Gaussian's fixed conic falloff with a kernel whose contour is defined by K learnable radial bases — $\alpha = o\exp(-\frac{1}{2}(\eta r_1^2 + (1-\eta) r_2^2 / \bar{s}^2))$ followed by a piecewise sharpening function $\Psi(g)$ — yields a strictly more expressive planar primitive that still reduces to a 2D Gaussian as a special case when K = 4 with orthogonal axes and paired equal scales. The paper argues this removes three built-in limitations of Gaussians: rotational symmetry, conic L2 boundaries that cannot make straight edges, and the coupling of spatial extent with edge sharpness. The consequence is a splatting method that fits rectangles, triangles, and curved boundaries with single kernels instead of clusters of Gaussians, and the experiments back this with higher PSNR and lower LPIPS on both synthetic and real unbounded scenes.

Load-bearing premise

The load-bearing premise is that the scene's camera poses are very accurate; the paper's own noise experiments show that with pose error at the level of $10^{-3}$ in the camera parameters, DRK loses its advantage and falls below Gaussian splatting on PSNR.

Editorial extensions

If this is right

  • On synthetic and real scenes with accurate poses, DRK matches or exceeds Gaussian-splatting baselines on PSNR, LPIPS, and SSIM while using drastically fewer primitives; at its sparsest setting it uses roughly 42K primitives versus about 336K for standard Gaussian splatting.
  • Sharp boundaries and straight edges — text, foliage silhouettes, specular rims — become representable by single kernels, so the number of kernels needed to approximate a shape no longer depends on its edge sharpness.
  • Because DRK is planar and its boundary is a polygon, triangle and polygon meshes can be converted into DRK kernels without training, letting traditional 3D assets be composited into reconstructed scenes.
  • The paper's own robustness study shows the quality advantage is conditional: under pose noise with standard deviation $10^{-3}$, DRK's PSNR drops from 37.58 to 31.59 while Gaussian splatting only drops from 34.41 to 33.44.
  • The cache-sorting mechanism, keyed to ray-plane intersection distance, is designed to prevent popping artifacts when overlapping kernels have identical center depths, which is especially relevant for the mesh-conversion application.

Reading between the lines

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

  • A testable consequence the authors leave implicit: the same sharpness parameters that give DRK its edge fidelity should make it more prone to overfit pose errors, since small misalignments create high-frequency residuals; a joint pose-and-kernel optimization would likely close most of the robustness gap.
  • The kernel formulation suggests a natural bridge to surface reconstruction: fit DRK's polygon boundary as a mesh proxy, giving a train-free way to extract geometry from splatting.
  • One could extend DRK to dynamic scenes by making the radial bases and angles time-dependent; the planar form is compatible with existing deformation machinery.
  • DRK's expressiveness per primitive might also benefit inverse rendering, where a single kernel with a sharp edge can model a material boundary without a separate segmentation.
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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

4 major / 6 minor

Summary. The paper introduces Deformable Radial Kernel (DRK) splatting, a planar primitive that generalizes 2D Gaussian splatting by using K learnable radial bases, an L1/L2 norm blend controlled by η, and a piecewise-linear sharpening parameter τ. A rendering pipeline is proposed with ray-plane intersection, polygon-based tile culling, a low-pass filter, and a cache-sorting scheme. Experiments compare DRK against 3D-GS, 2D-GS, 3D-HGS, and GES on a new DiverseScenes benchmark, Mip-NeRF360, NeRF-Synthetic, and Tanks & Temples, and the paper also demonstrates mesh-to-DRK conversion. The abstract claims state-of-the-art rendering quality with dramatically reduced primitive counts.

Significance. If the claims are properly scoped, DRK is a useful extension of Gaussian splatting: the derivation that a 2D Gaussian is a special case (Sec. 4.1) is sound, the L1/L2 blending and sharpening give the kernel additional expressive power, and the rasterization contributions (polygon culling, cache-sorting, low-pass filtering) are nontrivial. The authors also contribute a new benchmark. However, the headline SOTA claim is not supported on Mip-NeRF360 and is fragile under pose noise, so the significance depends on whether the method is presented as a specialized representation for accurate-pose settings.

major comments (4)
  1. [Abstract; Sec. 5.2, Tables 1 and 3] The abstract's claim that DRK 'achieves state-of-the-art performance while dramatically reducing primitive count' is not supported on the standard Mip-NeRF360 benchmark: Table 3 shows DRK's PSNR (26.76) is lower than 3D-GS (L) (26.94), and its primitive count (952K) is higher than 3D-GS (811K). The advantage there is primarily in LPIPS/SSIM, and the dramatic reduction only holds for the S2/S1 variants on DiverseScenes. Please revise the claims to state the specific regimes where DRK is superior.
  2. [Sec. S3, Tables S5 and S6] The robustness evaluation shows that DRK's advantage vanishes under realistic pose error: on DiverseScenes, adding Gaussian pose noise with std 1e-3 lowers DRK's average PSNR from 37.58 to 31.59, while 3D-GS only drops from 34.41 to 33.44 (Table S6); consistent with this, DRK underperforms 3D-GS on Tanks & Temples (20.41 vs 21.09, Table S5). Because COLMAP pose errors in natural captures often exceed 1e-3, the unqualified 'outperforms existing methods' claim in the abstract is not justified. The paper should either incorporate pose uncertainty into the representation or clearly state that the method targets settings with near-exact camera poses.
  3. [Sec. 5.2, Tables 2 and 3] The claim of 'dramatically reducing primitive count' is not consistent across experiments: full DRK uses 260K primitives on DiverseScenes (vs 346K for 3D-GS) and 952K on Mip-NeRF360 (vs 811K for 3D-GS), so the reduction is modest or reversed. The sparse variants (S1, S2) do reduce counts, but with lower PSNR. We recommend reporting results at matched primitive counts to substantiate the efficiency claim.
  4. [Sec. 4.4, Eq. (11)] The calibrated radial length is defined as sc_k = s_k sqrt(-log(Ψ^{-1}(e^{-32}/o))). The quantity e^{-32} appears to be a typo: if it is meant to encode the 3-σ boundary, the threshold should be exp(-9/2) or exp(-9) rather than exp(-32). Please clarify the derivation in the supplementary and ensure the culling radius is consistent with the kernel's actual support; an incorrect culling radius can affect both correctness and the claimed speedups.
minor comments (6)
  1. [Abstract] The phrase 'iven DRK's planar nature' is missing the leading 'G'; please correct it to 'Given DRK's planar nature'.
  2. [Tables 1 and S1] The column header 'A verage' should be 'Average'.
  3. [Sec. 4.4, Eq. (11)] Beyond the numerical issue noted above, the notation 'e^{-32}' is ambiguous; please define what the superscript represents.
  4. [Sec. S3] The name 'Tank&Temple' is written inconsistently; please use 'Tanks and Temples' throughout.
  5. [Sec. 4.4] The reference to StopThePop [42] appears with broken citation markers ('6, 4, 5'); please fix the citation formatting.
  6. [Figure 1] The labels 'GS (50)', 'GS (500)', 'GS (5K)', 'DRK (50)' are not defined; state what the numbers denote (e.g., number of primitives).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DRK is a novel fitted kernel representation; the claimed Gaussian-special-case reduction is a direct algebraic identity, and self-citations are not load-bearing.

full rationale

The paper's central contribution is a new parametric kernel (Eqs. 6-9) whose parameters (scales, basis angles, opacity, sharpness, curvature weight) are optimized by standard gradient descent against multi-view training images; no quantity is fitted and then relabeled as a prediction. The claim that DRK reduces to a 2D Gaussian is proven in Sec. 4.1 by substituting K=4, theta_k=k*pi/2, and alternating scales into Eq. (6) and obtaining Eq. (3) via the identity cos(2theta)=2cos^2(theta)-1; this is a self-contained algebraic check, not an imported assumption. The rasterization components (ray-plane intersection, polygon tile culling, cache-sorting, low-pass filtering) are constructive engineering choices, with the low-pass filter and sorting ideas attributed to external prior work (Botsch et al., StopThePop). Author self-citations appear only in the related-work survey and in the choice of MiVOS for foreground masking; none carries the load of the central representation claim. The paper is evaluated against official implementations of 3D-GS, 2D-GS, 3D-HGS, and GES on an introduced dataset and on external benchmarks (Mip-NeRF360, NeRF-Synthetic), so the headline result has independent empirical content. The paper's own robustness study (Table S6) and Tank&Temple results (Table S5) show a real fragility to camera pose error, but that is a correctness or scope limitation on the unqualified SOTA claim, not a circularity: DRK's loss under pose noise does not mean its derivation assumes its conclusion.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

DRK is a new mathematical primitive, not a physical entity; no new forces, particles, or fields are introduced. The model's learnable parameters (radial bases, angles, scales, eta, tau) are fit per scene via standard optimization, which is the normal use of such a representation and not a set of fitted constants in a derivation. The hand-set hyperparameters listed above, especially K and the densification thresholds, directly affect the central efficiency claim.

free parameters (4)
  • Number of radial bases K = 8
    Set by hand to balance flexibility and memory; ablation shows K=3 hurts performance significantly (Table S4).
  • Low-pass filter radius s_l = not specified
    Introduced in Eq. 10 to control anti-aliasing; no value or learning rule is given.
  • Cache length = 8
    Used in cache-sorting; claimed sufficient, but no sensitivity analysis besides Figure S4.
  • Densification/opacity thresholds = (5e-4,5e-2), (1e-3,5e-2), (2e-3,1e-1)
    Three configurations S, S1, S2; these control the primitive count, which is central to the efficiency claim.
assumptions (6)
  • domain assumption Scenes can be represented by a set of 2D planar kernels with local (u,v) coordinates and alpha blending (general kernel splatting, Sec. 3.3).
    Inherited from 2D-GS; if surfaces are not locally planar, DRK's geometric grounding weakens.
  • standard math The ray-plane intersection and perspective projection equations (Eq. 4-5) exactly define the kernel's screen-space contribution.
    Basic analytic geometry; needed for the kernel evaluation.
  • domain assumption The neural point-based alpha blending of 3D-GS (Eq. 1) remains valid for arbitrary planar kernel opacities.
    Assumes that per-primitive opacity products produce correct compositing for non-Gaussian footprints.
  • ad hoc to paper The sharpening function Psi(g) (Eq. 9) is continuous and monotone on [0,1].
    Design choice; it maps [0,1] to [0,1] and is piecewise linear, but is not derived from any first principle.
  • ad hoc to paper The low-pass filter max(alpha, filter) (Eq. 10) adequately prevents aliasing and floating primitives.
    Borrowed from surface splatting; the specific cosine view-scaling is heuristic.
  • ad hoc to paper Cache-sorting with cache length 8 approximates exact depth order well enough to avoid popping artifacts.
    Empirically validated in Figure S4, but not a formal guarantee.

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Cite this review

Pith. "Pith review of Deformable Radial Kernel Splatting." pith.science (2026). https://pith.science/paper/HWRRAHYT

@misc{pith2026241211752,
  author       = {Pith},
  title        = {Pith review of: Deformable Radial Kernel Splatting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HWRRAHYT}},
  note         = {Machine review of arXiv:2412.11752}
}
read the original abstract

Recently, Gaussian splatting has emerged as a robust technique for representing 3D scenes, enabling real-time rasterization and high-fidelity rendering. However, Gaussians' inherent radial symmetry and smoothness constraints limit their ability to represent complex shapes, often requiring thousands of primitives to approximate detailed geometry. We introduce Deformable Radial Kernel (DRK), which extends Gaussian splatting into a more general and flexible framework. Through learnable radial bases with adjustable angles and scales, DRK efficiently models diverse shape primitives while enabling precise control over edge sharpness and boundary curvature. iven DRK's planar nature, we further develop accurate ray-primitive intersection computation for depth sorting and introduce efficient kernel culling strategies for improved rasterization efficiency. Extensive experiments demonstrate that DRK outperforms existing methods in both representation efficiency and rendering quality, achieving state-of-the-art performance while dramatically reducing primitive count.

Figures

Figures reproduced from arXiv: 2412.11752 by the authors.

Figure 1
Figure 1. Gaussian Splatting vs. Our Deformable Radial Kernel (DRK) Splatting: Gaussian splatting requires thousands of Gaussians to approximate detailed textures and shapes. In contrast, our kernel efficiently fits the target pattern with just 30 primitives, achieving superior results. Abstract Recently, Gaussian splatting has emerged as a robust tech￾nique for representing 3D scenes, enabling real-time ras￾terization and hi… view at source ↗
Figure 3
Figure 3. Illustration of general planar kernel splatting: UV coor [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 2
Figure 2. Comparison of 3D-GS versus a single DRK: DRK achieves superior geometric fidelity with just one primitive compared to multiple Gaussians. We visualize the contours of 3D-GS and DRK to better illustrate primitive count, scale, and position. These constraints necessitate millions of fine-grained Gaussians to approximate arbitrary shapes, leading to over￾parameterization while still failing to achieve perfect fi￾delity… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: DRK defines a deformable shape using radial basis func￾tions characterized by lengths sk and polar angles θk, with pa￾rameters η and τ governing the shape’s curvature and sharpness respectively. 4.1. Radial Basis Our radial basis defines a kernel’s shape through K cont…
Figure 5
Figure 5. Figure 5: Sharpening function illustration. As shown in [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Polygon-based tile culling using radial basis endpoints [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Our cache-sorting uses [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Qualitative comparisons of DRK with state-of-the-art methods across various scenarios show that DRK effectively captures sharp texture and geometry boundaries. is followed by our sparser version, DRK (S1). DRK (S2) also performs comparably to other state-of-the-art met…
Figure 9
Figure 9. Figure 9: Comparisons on the MipNeRF 360 dataset show our method achieves clearer details and high-fidelity rendering. [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: The conversion from Mesh to DRK demonstrates the efficiency of using DRK to incorporate traditional 3D assets into any DRK scene at low cost. 6. Conclusion We present DRK, a novel primitive representation that gen￾eralizes and enhances Gaussian splatting. By incorpora…

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Forward citations

Cited by 3 Pith papers

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  3. Quadratic Gaussian Splatting: High Quality Surface Reconstruction with Second-order Geometric Primitives

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Reference graph

Works this paper leans on

66 extracted references · 45 canonical work pages · cited by 3 Pith papers

  1. [1]

    Neural point-based graph- ics

    Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graph- ics. In Proceedings of the European Conference on Com- puter Vision (ECCV), pages 696–712. Springer, 2020. 2

    work page 2020

  2. [2]

    Mip-NeRF 360: Unbounded anti-aliased neural radiance fields

    Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-NeRF 360: Unbounded anti-aliased neural radiance fields. In CVPR, pages 5470– 5479, 2022. 2, 6, 7, 3

    work page 2022

  3. [3]

    NeRD: Neural reflectance decomposition from image collections

    Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Bar- ron, Ce Liu, and Hendrik Lensch. NeRD: Neural reflectance decomposition from image collections. In ICCV, pages 12684–12694, 2021. 2

    work page 2021

  4. [4]

    High-quality surface splatting on today’s gpus

    Mario Botsch, Alexander Hornung, Matthias Zwicker, and Leif Kobbelt. High-quality surface splatting on today’s gpus. In Proceedings Eurographics/IEEE VGTC Symposium Point-Based Graphics, 2005., pages 17–141. IEEE, 2005. 5

    work page 2005

  5. [5]

    Unstructured lumigraph ren- dering

    Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael Cohen. Unstructured lumigraph ren- dering. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques , pages 425– 432, 2001. 2

    work page 2001

  6. [6]

    Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion

    Ho Kei Cheng, Yu-Wing Tai, and Chi-Keung Tang. Modular interactive video object segmentation: Interaction-to-mask, propagation and difference-aware fusion. In CVPR, 2021. 7

    work page 2021

  7. [7]

    High-quality surface recon- struction using gaussian surfels

    Pinxuan Dai, Jiamin Xu, Wenxiang Xie, Xinguo Liu, Huamin Wang, and Weiwei Xu. High-quality surface recon- struction using gaussian surfels. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 1, 3

    work page 2024

  8. [8]

    Unstructured light fields

    Abe Davis, Marc Levoy, and Fredo Durand. Unstructured light fields. Computer Graphics Forum, 31(2pt1):305–314,

    work page

Show all 66 references
  1. [9]

    Mod- eling and rendering architecture from photographs: A hybrid geometry-and image-based approach

    Paul E Debevec, Camillo J Taylor, and Jitendra Malik. Mod- eling and rendering architecture from photographs: A hybrid geometry-and image-based approach. In Proceedings of the 23rd annual conference on Computer graphics and interac- tive techniques, pages 11–20, 1996. 2

    1996

  2. [10]

    4d gaussian splatting: Towards efficient novel view synthesis for dynamic scenes

    Yuanxing Duan, Fangyin Wei, Qiyu Dai, Yuhang He, Wen- zheng Chen, and Baoquan Chen. 4d gaussian splatting: Towards efficient novel view synthesis for dynamic scenes. arXiv preprint arXiv:2402.03307, 2024. 3

    2024 arXiv

  3. [11]

    Unified implicit neural styliza- tion

    Zhiwen Fan, Yifan Jiang, Peihao Wang, Xinyu Gong, Dejia Xu, and Zhangyang Wang. Unified implicit neural styliza- tion. In ECCV, pages 636–654, 2022. 2

    2022

  4. [12]

    DeepView: View synthesis with learned gradient descent

    John Flynn, Michael Broxton, Paul Debevec, Matthew Du- Vall, Graham Fyffe, Ryan Overbeck, Noah Snavely, and Richard Tucker. DeepView: View synthesis with learned gradient descent. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) , p...

    2019

  5. [13]

    Plenoxels: Radiance fields without neural networks

    Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, pages 5501–5510, 2022. 2

    2022

  6. [14]

    Relightable 3d gaussian: Real-time point cloud relighting with brdf decomposition and ray trac- ing

    Jian Gao, Chun Gu, Youtian Lin, Hao Zhu, Xun Cao, Li Zhang, and Yao Yao. Relightable 3d gaussian: Real-time point cloud relighting with brdf decomposition and ray trac- ing. arXiv preprint arXiv:2311.16043, 2023. 1

    2023 arXiv

  7. [15]

    SurfelNeRF: Neural surfel radiance fields for online photorealistic recon- struction of indoor scenes

    Yiming Gao, Yan-Pei Cao, and Ying Shan. SurfelNeRF: Neural surfel radiance fields for online photorealistic recon- struction of indoor scenes. arXiv preprint arXiv:2304.08971,

    arXiv

  8. [16]

    The lumigraph

    Steven J Gortler, Radek Grzeszczuk, Richard Szeliski, and Michael F Cohen. The lumigraph. In Proceedings of the 23rd annual conference on Computer graphics and interac- tive techniques, pages 43–54, 1996. 2

    1996

  9. [17]

    Sugar: Surface- aligned gaussian splatting for efficient 3d mesh reconstruc- tion and high-quality mesh rendering

    Antoine Gu ´edon and Vincent Lepetit. Sugar: Surface- aligned gaussian splatting for efficient 3d mesh reconstruc- tion and high-quality mesh rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5354–5363, 2024. 1, 3

    2024

  10. [18]

    Ges: Generalized exponential splatting for efficient radiance field rendering

    Abdullah Hamdi, Luke Melas-Kyriazi, Jinjie Mai, Guocheng Qian, Ruoshi Liu, Carl V ondrick, Bernard Ghanem, and Andrea Vedaldi. Ges: Generalized exponential splatting for efficient radiance field rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patter...

    2024

  11. [19]

    3D con- vex splatting: Radiance field rendering with 3D smooth con- vexes

    Jan Held, Renaud Vandeghen, Abdullah Hamdi, Adrien Deli‘ege, Anthony Cioppa, Silvio Giancola, Andrea Vedaldi, Bernard Ghanem, and Marc Van Droogenbroeck. 3D con- vex splatting: Radiance field rendering with 3D smooth con- vexes. arXiv, abs/2411.14974, 2024. 2, 3

    2024 arXiv

  12. [20]

    2d gaussian splatting for geometrically ac- curate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometrically ac- curate radiance fields. In ACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024. 1, 2, 3, 6, 8

    2024

  13. [21]

    StylizedNeRF: consistent 3D scene stylization as styl- ized NeRF via 2D-3D mutual learning

    Yi-Hua Huang, Yue He, Yu-Jie Yuan, Yu-Kun Lai, and Lin Gao. StylizedNeRF: consistent 3D scene stylization as styl- ized NeRF via 2D-3D mutual learning. In CVPR, pages 18342–18352, 2022. 2

    2022

  14. [22]

    Nerf-texture: Texture synthesis with neural radi- ance fields

    Yi-Hua Huang, Yan-Pei Cao, Yu-Kun Lai, Ying Shan, and Lin Gao. Nerf-texture: Texture synthesis with neural radi- ance fields. In ACM SIGGRAPH 2023 Conference Proceed- ings, pages 1–10, 2023. 2

    2023

  15. [23]

    Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes

    Yi-Hua Huang, Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei Cao, and Xiaojuan Qi. Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4220–4230, 2024. 2

    2024

  16. [24]

    Relu fields: The little non-linearity that could

    Animesh Karnewar, Tobias Ritschel, Oliver Wang, and Niloy Mitra. Relu fields: The little non-linearity that could. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9,

    2022

  17. [25]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1,

  18. [26]

    Tanks and temples: Benchmarking large-scale scene reconstruction

    Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics, 36(4), 2017. 2

    2017

  19. [27]

    NeROIC: Neural rendering of objects from online image collections

    Zhengfei Kuang, Kyle Olszewski, Menglei Chai, Zeng Huang, Panos Achlioptas, and Sergey Tulyakov. NeROIC: Neural rendering of objects from online image collections. ACM Trans. Graph., 41(4):1–12, 2022. 2

    2022

  20. [28]

    Light field rendering

    Marc Levoy and Pat Hanrahan. Light field rendering. In Pro- ceedings of the 23rd annual conference on Computer graph- ics and interactive techniques, pages 31–42, 1996. 2

    1996

  21. [29]

    3d-hgs: 3d half-gaussian splatting

    Haolin Li, Jinyang Liu, Mario Sznaier, and Octavia Camps. 3d-hgs: 3d half-gaussian splatting. arXiv preprint arXiv:2406.02720, 2024. 1, 3, 6, 8

    2024 arXiv

  22. [30]

    DeVRF: Fast deformable voxel ra- diance fields for dynamic scenes

    Jia-Wei Liu, Yan-Pei Cao, Weijia Mao, Wenqiao Zhang, David Junhao Zhang, Jussi Keppo, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. DeVRF: Fast deformable voxel ra- diance fields for dynamic scenes. In NeurIPS, pages 36762– 36775, 2022. 2

    2022

  23. [31]

    Neural vol- umes: Learning dynamic renderable volumes from images

    Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural vol- umes: Learning dynamic renderable volumes from images. ACM Transactions on Graphics (TOG), 2019. 2

    2019

  24. [32]

    Scaffold-gs: Structured 3d gaussians for view-adaptive rendering

    Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai. Scaffold-gs: Structured 3d gaussians for view-adaptive rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20654–20664, 2024. 2

    2024

  25. [33]

    Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis

    Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. arXiv preprint arXiv:2308.09713, 2023. 2

    2023 arXiv

  26. [34]

    3dgsr: Implicit surface reconstruction with 3d gaussian splat- ting

    Xiaoyang Lyu, Yang-Tian Sun, Yi-Hua Huang, Xiuzhe Wu, Ziyi Yang, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi. 3dgsr: Implicit surface reconstruction with 3d gaussian splat- ting. arXiv preprint arXiv:2404.00409, 2024. 2

    2024 arXiv

  27. [35]

    Neural rerendering in the wild

    Moustafa Meshry, Dan B Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, and Ricardo Martin- Brualla. Neural rerendering in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6878–6887, 2019. 2

    2019

  28. [36]

    Local light field fusion: Practical view syn- thesis with prescriptive sampling guidelines

    Ben Mildenhall, Pratul P Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar. Local light field fusion: Practical view syn- thesis with prescriptive sampling guidelines. ACM Transac- tions on Graphics (TOG), 38(4):1–14, 2019. 2

    2019

  29. [37]

    NeRF: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM , 65(1):99–106, 2021. 2

    2021

  30. [38]

    Instant neural graphics primitives with a mul- tiresolution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a mul- tiresolution hash encoding. ACM Trans. Graph., 41(4):1–15,

  31. [39]

    3D Ken Burns effect from a single image

    Simon Niklaus, Long Mai, Jimei Yang, and Feng Liu. 3D Ken Burns effect from a single image. ACM Transactions on Graphics (TOG), 38(6):1–15, 2019. 2

    2019

  32. [40]

    Neu- Physics: Editable neural geometry and physics from monoc- ular videos

    Yi-Ling Qiao, Alexander Gao, and Ming Lin. Neu- Physics: Editable neural geometry and physics from monoc- ular videos. In NeurIPS, pages 12841–12854, 2022. 2

    2022

  33. [41]

    Disc-gs: Discontinuity-aware gaussian splatting

    Haoxuan Qu, Zhuoling Li, Hossein Rahmani, Yujun Cai, and Jun Liu. Disc-gs: Discontinuity-aware gaussian splatting. arXiv preprint arXiv:2405.15196, 2024. 1, 3

    2024 arXiv

  34. [42]

    Stopthepop: Sorted gaussian splatting for view-consistent real-time rendering

    Lukas Radl, Michael Steiner, Mathias Parger, Alexan- der Weinrauch, Bernhard Kerbl, and Markus Steinberger. Stopthepop: Sorted gaussian splatting for view-consistent real-time rendering. ACM Transactions on Graphics (TOG), 43(4):1–17, 2024. 6, 4, 5

    2024

  35. [43]

    Free view synthesis

    Gernot Riegler and Vladlen Koltun. Free view synthesis. In Proceedings of the European Conference on Computer Vi- sion (ECCV), pages 623–640. Springer, 2020. 2

    2020

  36. [44]

    Stable view synthesis

    Gernot Riegler and Vladlen Koltun. Stable view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 12216–12225,

  37. [45]

    Structure-from-motion revisited

    Johannes Lutz Sch ¨onberger and Jan-Michael Frahm. Structure-from-motion revisited. In CVPR, 2016. 6, 3

    2016

  38. [46]

    Photorealistic scene reconstruction by voxel coloring

    Steven M Seitz and Charles R Dyer. Photorealistic scene reconstruction by voxel coloring. International Journal of Computer Vision, 35(2):151–173, 1999. 2

    1999

  39. [47]

    Deep- V oxels: Learning persistent 3D feature embeddings

    Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhofer. Deep- V oxels: Learning persistent 3D feature embeddings. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2437–2446, 2019. 2

    2019

  40. [48]

    NeRF- Player: A streamable dynamic scene representation with de- composed neural radiance fields

    Liangchen Song, Anpei Chen, Zhong Li, Zhang Chen, Lele Chen, Junsong Yuan, Yi Xu, and Andreas Geiger. NeRF- Player: A streamable dynamic scene representation with de- composed neural radiance fields. IEEE TVCG, 29(5):2732– 2742, 2023. 2

    2023

  41. [49]

    Pushing the boundaries of view extrapolation with multiplane images

    Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely. Pushing the boundaries of view extrapolation with multiplane images. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), pages 175–184...

    2019

  42. [50]

    Splatter a video: Video gaus- sian representation for versatile processing

    Yang-Tian Sun, Yi-Hua Huang, Lin Ma, Xiaoyang Lyu, Yan- Pei Cao, and Xiaojuan Qi. Splatter a video: Video gaus- sian representation for versatile processing. arXiv preprint arXiv:2406.13870, 2024. 2

    2024 arXiv

  43. [51]

    Block-NeRF: Scalable large scene neural view synthesis

    Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Prad- han, Ben Mildenhall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar. Block-NeRF: Scalable large scene neural view synthesis. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Reco...

    2022

  44. [52]

    Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation

    Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation. arXiv preprint arXiv:2309.16653,

    arXiv

  45. [53]

    Single-view view synthe- sis with multiplane images

    Richard Tucker and Noah Snavely. Single-view view synthe- sis with multiplane images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 551–560, 2020. 2

    2020

  46. [54]

    Let there be color! large-scale texturing of 3D reconstructions

    Michael Waechter, Nils Moehrle, and Michael Goesele. Let there be color! large-scale texturing of 3D reconstructions. In Proceedings of the European conference on computer vi- sion (ECCV), pages 836–850. Springer, 2014. 2

    2014

  47. [55]

    SynSin: End-to-end view synthesis from a sin- gle image

    Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. SynSin: End-to-end view synthesis from a sin- gle image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7467–7477, 2020. 2

    2020

  48. [56]

    NeX: Real-time view synthesis with neural basis expansion

    Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, and Supasorn Suwajanakorn. NeX: Real-time view synthesis with neural basis expansion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 8534–8543, 2021. 2

    2021

  49. [57]

    Surface light fields for 3D photography

    Daniel N Wood, Daniel I Azuma, Ken Aldinger, Brian Cur- less, Tom Duchamp, David H Salesin, and Werner Stuetzle. Surface light fields for 3D photography. In Proceedings of the 27th annual conference on Computer graphics and inter- active techniques, pages 287–296, 2000. 2

    2000

  50. [58]

    Sire-ir: Inverse rendering for brdf reconstruction with shadow and illumi- nation removal in high-illuminance scenes

    Ziyi Yang, Yanzhen Chen, Xinyu Gao, Yazhen Yuan, Yu Wu, Xiaowei Zhou, and Xiaogang Jin. Sire-ir: Inverse rendering for brdf reconstruction with shadow and illumi- nation removal in high-illuminance scenes. arXiv preprint arXiv:2310.13030, 2023. 2

    arXiv

  51. [59]

    Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting

    Zeyu Yang, Hongye Yang, Zijie Pan, and Li Zhang. Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting. arXiv preprint arXiv:2310.10642, 2023. 3

    2023 arXiv

  52. [60]

    Spec-gaussian: Anisotropic view-dependent appearance for 3d gaussian splatting

    Ziyi Yang, Xinyu Gao, Yangtian Sun, Yihua Huang, Xi- aoyang Lyu, Wen Zhou, Shaohui Jiao, Xiaojuan Qi, and Xiaogang Jin. Spec-gaussian: Anisotropic view-dependent appearance for 3d gaussian splatting. arXiv preprint arXiv:2402.15870, 2024. 2

    2024 arXiv

  53. [61]

    Deformable 3d gaussians for high- fidelity monocular dynamic scene reconstruction

    Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high- fidelity monocular dynamic scene reconstruction. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20331–20341, 2024. 2

    2024

  54. [62]

    NeRF++: Analyzing and improving neural radiance fields

    Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. NeRF++: Analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.07492, 2020. 2

    2010 arXiv

  55. [63]

    ARF: Artistic radiance fields

    Kai Zhang, Nick Kolkin, Sai Bi, Fujun Luan, Zexiang Xu, Eli Shechtman, and Noah Snavely. ARF: Artistic radiance fields. In ECCV, pages 717–733, 2022. 2

    2022

  56. [64]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 6

    2018

  57. [65]

    Stereo magnification: Learning view synthesis using multiplane images

    Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM Transactions on Graphics (TOG), 2018. 2

    2018

  58. [66]

    Triplane meets gaussian splatting: Fast and generalizable single-view 3d reconstruction with transformers

    Zi-Xin Zou, Zhipeng Yu, Yuan-Chen Guo, Yangguang Li, Ding Liang, Yan-Pei Cao, and Song-Hai Zhang. Triplane meets gaussian splatting: Fast and generalizable single-view 3d reconstruction with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern...

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.