Pith. sign in

REVIEW 4 major objections 6 minor 61 references

Efficient multi-view training for 3D Gaussian Splatting

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read This paper claims that single-view mini-batch training is the bottleneck in 3DGS and that multi-view training, made efficient with partial rendering and a 3D-aware D-SSIM loss, consistently improves reconstruction quality.

desk verdict Solid engineering contribution with a weakly supported theoretical framing and an unablated hyperparameter confound; worth a serious referee, but needs ablation and significance data. read the letter →

arxiv 2506.12727 v2 pith:5QDO7WVE submitted 2025-06-15 cs.CV

Minhyuk Choi , Injae Kim , Hyunwoo J. Kim This is my paper
classification cs.CV
keywords 3DGaussianSplattingmulti-viewtrainingmini-batchgradientvariancepartialrenderingdistance-awareD-SSIMadaptivedensitycontrol3DGS-MCMCnovelviewsynthesis
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

3D Gaussian Splatting is normally trained one image at a time, unlike NeRF, which averages over many rays from many views. This paper argues that single-view training is a bottleneck: the stochastic gradients from a single image have larger variance than a multi-view mini-batch, so optimization is noisier and the final scene is worse. To remove that bottleneck without paying the full cost of rendering several full images, the paper modifies the rasterizer to render a fraction of each viewpoint's pixels per iteration, replaces the 2D D-SSIM loss with a 3D distance-aware version, and repairs the densification rule so gradients from different image planes are not added as if they lived in one plane. On standard benchmarks it reports consistent PSNR, SSIM, and LPIPS gains for both 3DGS and 3DGS-MCMC, in some settings at shorter training time. A sympathetic reader should take away that multi-view mini-batching is not just a NeRF habit but a generally applicable improvement once the renderer and losses are adapted.

What carries the argument

The engine of the argument is Lemma 1, a variance identity for sample means: if one draws $K$ samples from each of $N$ distributions with distinct means, the variance of the resulting mini-batch mean is proportional to $1/m$, where $m$ is the number of distributions sampled, so drawing from all distributions ($m=N$) minimizes variance and drawing from a single distribution ($m=1$) maximizes it. Proposition 1 lifts this to gradients: when the clusters of input data match the clusters of gradient vectors, sampling across clusters reduces mini-batch gradient variance, and the paper applies this to the camera viewpoints acting as clusters. The engineering machinery that makes the principle usable in 3DGS is threefold: thread-efficient partial rendering, which splits each tile across small thread blocks so masked-out pixels never leave threads idle; a 3D distance-aware D-SSIM loss, which replaces the 2D Gaussian kernel $K_\sigma$ with a kernel $K^*_\sigma(u,v)=(1/2\pi\sigma^2)\exp(-(X^2+Y^2+Z^2)/2\sigma^2)$ built from the world-space position of each pixel computed through predicted depth; and multi-view adaptive density control, which substitutes invalid sums of 2D positional gradients in different image planes with the space-independent metrics $E_1(G)=\sum_{k}\sum_{V_{p_i}=k}\|\nabla_{p_i}L\|_2$ and $E_2(G)=\sum_k\|\sum_{V_{p_i}=k}\nabla_{p_i}L\|_2$. Together, these pieces carry the variance-reduction principle into 3DGS without the full cost of multi-view rendering.

What would settle it

Freeze a trained or mid-training 3DGS model, sample many single-view and many four-view mini-batches, and compute the per-parameter gradient variance for the actual Gaussian means, scales, rotations, colors, and opacities; if single-view variance is not substantially larger than multi-view variance, the proposed explanation is wrong even if the methods still improve the metrics. Repeating the paper's Fig. 2 measurement on the rasterized Gaussian parameters rather than a proxy would settle whether the variance reduction actually occurs where it is claimed.

Watch

Extended reading notes

Core claim

The paper's central claim is that the de facto single-view mini-batch training of 3DGS is suboptimal, and that multi-view training is strictly better once it is made affordable. The core discovery is a variance argument: for a fixed number of samples, a mini-batch that draws from all N distributions (viewpoints) has sample-mean variance proportional to 1/N, while drawing all samples from one distribution leaves variance roughly N times larger, so multi-view gradients are closer to the true batch gradient and give more stable optimization. The paper then makes multi-view practical for 3DGS with three changes: a thread-efficient partial rendering that renders a sub-sampled set of pixels per viewpoint without idle GPU threads; a 3D distance-aware D-SSIM loss that measures structural similarity from 3D distances rather than 2D pixel neighborhoods, so pixels from different viewpoints are not forced to be similar; and a multi-view adaptive density control that replaces invalid sums of 2D positional gradients from different image planes with a sum of norms. With these pieces, training 3DGS and 3DGS-MCMC with four views per iteration improves PSNR, SSIM, and LPIPS over the single-view baselines across MipNeRF-360, Deep Blending, Tanks and Temples, and OMMO.

Load-bearing premise

The load-bearing assumption is that each camera viewpoint produces its own distinct cluster of per-image gradients for the Gaussian parameters, so mixing viewpoints lowers the variance of the mini-batch gradient; if the gradients from one viewpoint are just samples of the same distribution as any other, the variance argument collapses, though the empirical gains could still hold.

Editorial extensions

If this is right

  • Training 3DGS with four views per iteration instead of one produces higher PSNR/SSIM and lower LPIPS on MipNeRF-360, Deep Blending, Tanks and Temples, and OMMO, with the same or lower wall-clock time when partial rendering and halved iterations are used.
  • The multi-view benefit is not specific to the original 3DGS: adding only the multi-view strategy to 3DGS-MCMC raises its numbers, so the method transfers to variants that do not use adaptive density control.
  • The densification fix matters: multi-view training without the modified density control underperforms multi-view training with it, because summing 2D positional gradients across different image planes can cancel to zero.
  • D-SSIM cannot simply be applied per viewpoint in partial rendering; the 3D distance-aware version recovers the structural-similarity benefit, and removing it costs PSNR, SSIM, and LPIPS.
  • Because multi-view training changes the learning dynamics, the paper scales the pruning threshold by the number of images for 3DGS and divides the mean noise learning rate by the number of images for 3DGS-MCMC.

Reading between the lines

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

  • If the variance mechanism is what drives the gain, then selecting a diverse or well-separated set of viewpoints per iteration should reduce gradient variance further than random selection, since Lemma 1's benefit comes from sampling across distinct gradient clusters; the paper does not test view-selection strategies.
  • The 3D distance-aware D-SSIM loss suggests a general recipe for any renderer that composites pixels into a common buffer: replace 2D structural-similarity windows with kernels weighted by world-space distance, using predicted depth, to avoid penalizing discontinuities that are real scene boundaries.
  • The same thread-efficient partial-rendering idea could be applied beyond training, for example to render only masked or sparse regions in an image while keeping tile-based rasterization fully occupied, which would benefit tasks like scene editing or segmentation-guided rendering.
  • More than four views per iteration keeps improving PSNR in the paper's table, but at higher memory and preprocess cost; an adaptive budget that grows the number of views only when gradient variance is high could capture most of the benefit more cheaply.
Share X Bluesky LinkedIn Reddit HN

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 argues that the de facto single-view mini-batch training of 3D Gaussian Splatting (3DGS) is suboptimal and proposes multi-view mini-batch training. To support this, it presents a variance-reduction argument (Lemma 1 and Proposition 1) and empirical gradient-variance plots (Fig. 2). It then introduces three components: thread-efficient partial rendering to reduce the cost of rendering multiple images per iteration, a 3D distance-aware D-SSIM loss computed in world space, and a multi-view adaptive density control that avoids summing 2D positional gradients across different viewpoints. Experiments on MipNeRF-360, OMMO, Deep Blending, and Tanks and Temples report consistent improvements over 3DGS and 3DGS-MCMC, with ablations for each component, a comparison of rendering strategies, and a matched-time experiment.

Significance. If the central causal claim holds, the paper is a practically valuable contribution: it turns a ubiquitous but undocumented training choice in 3DGS into a deliberate, better-performing protocol, and the proposed rasterizer modification is a genuine engineering contribution that could benefit downstream 3DGS variants. The paper is commendably transparent in several respects: it discloses the additional hyperparameter scalings in Sec. 10, reports per-scene results in Tab. 7, provides pseudocode for the rasterizer changes, and includes a matched-time control in Tab. 5. The main weakness is that the disclosed hyperparameter changes are not ablated, and the matched-time gains are small and reported without uncertainty, so the empirical core does not yet isolate the effect of multi-view training itself.

major comments (4)
  1. [Sec. 10, Tabs. 1 and 3] The paper changes two hyperparameters when switching to multi-view training—the pruning threshold is multiplied by the number of images B in 3DGS, and the mean-noise learning rate is divided by B in 3DGS-MCMC—but no ablation isolates these scalings. The 3DGS-MCMC row in Tab. 1 (29.83 to 30.42) is labeled 'only the multi-view training strategy is applied', yet it also includes the learning-rate division from Sec. 10. To support the claim that multi-view training itself is effective, the authors should compare single-view training with the same hyperparameter scaling against multi-view training with and without scaling. Similarly, Tab. 3 does not state whether the pruning threshold scaling is applied in the '✗' condition, so the improvement attributed to multi-view ADC could be confounded with the threshold change.
  2. [Tabs. 1, 4, and 5] The headline improvements are obtained at substantially increased wall-clock time (e.g., 50m vs. 26m for 3DGS in Tabs. 4 and 5), and the matched-time comparison in Tab. 5 shows only +0.15 PSNR for 3DGS and +0.42 for 3DGS-MCMC. No standard deviations or per-seed results are reported, despite the paper stating that three repeats were averaged. With gains of this size, run-to-run variability in 3DGS could be comparable to the reported improvement. The authors should report error bars or per-seed values and, ideally, a significance test for the matched-time comparison, since this is the experiment that directly supports the 'comparable or lower training time' claim.
  3. [Sec. 8.1, Proposition 1, and Fig. 2] The transfer of the variance-reduction Lemma 1 to 3DGS rests on the unverified assumption that 'clusters of x are the same as the clusters of psi(x,y)' for 3DGS rasterization parameters. The paper provides no evidence that each viewpoint forms a gradient cluster for Gaussian means, scales, or colors, and Fig. 2 does not specify which parameters were used for the 3DGS variance measurement or how the variance was computed. Because the theoretical analysis is a stated contribution and is used to motivate the method, the authors should either provide a concrete test of the cluster assumption (e.g., gradient-covariance analysis per parameter group and per viewpoint) or explicitly reframe Proposition 1 as motivation rather than proof.
  4. [Tab. 3 and Sec. 5.2.2] The multi-view ADC ablation is not sufficient to validate the proposed E1/E2 error metrics. The table contrasts 'Multi-view ADC ✗' with '✓', but it does not describe what the ✗ condition does when gradients arrive from multiple viewpoints—does it naively sum 2D gradients in different spaces, and is the pruning threshold scaling applied? Without these details, the +0.18 PSNR improvement cannot be uniquely attributed to the space-independent error metric.
minor comments (6)
  1. [Eq. (6), Sec. 4.2] Equation (6) writes 'P(w_t)' where a gradient is meant; as written, P(w_t) is the scalar objective, so the displayed expression is dimensionally inconsistent. Please replace it with ∇P(w_t) throughout Sec. 4.2.
  2. [Fig. 2] The two panels in Fig. 2 lack axis labels and the caption does not state which parameters were used for the 3DGS variance computation; please add these details and describe how the Monte Carlo estimate in Eq. (6) was instantiated.
  3. [Sec. 12 and Tab. 7] The Limitations section acknowledges overfitting in the bicycle/stump and Deep Blending scenes; this should be mentioned in the main results discussion, since Tab. 7 shows per-scene degradations such as Stump (26.88 to 26.70) for 3DGS.
  4. [Tab. 5] The description 'halving the training iterations and the iteration-related hyperparameters' is vague; please specify which hyperparameters were halved (e.g., densification interval, warm-up steps), so the matched-time comparison is reproducible.
  5. [Algorithm 3] The pseudocode in Algorithm 3 would benefit from a note that the K blocks run concurrently and from a clearer definition of the index array A, since the current 'for block' / 'for thread' nesting is easy to misread as sequential.
  6. [References] Reference [13] appears as an arXiv preprint, but 3DGS-MCMC was published at ICCV 2024; please update the citation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the variance-reduction argument is an explicit conditional result and the experimental gains are measured against independent baselines, not derived from fitted parameters.

full rationale

The paper's central derivation chain is: Lemma 1 establishes a general variance comparison for stratified sampling from distributions with unequal means; Proposition 1 applies it to mini-batch gradients under an explicitly stated clustering assumption; and the empirical variance measurements (Fig. 2) plus the full 3DGS experiments test the resulting hypothesis. None of these steps defines its conclusion into its premises. Lemma 1 is proved from the definitions of variance and the law of total variance via equations (15)-(43), not imported from a self-citation. The 3D distance-aware D-SSIM loss and multi-view adaptive density control are new components evaluated by ablations against external baselines and against conventional losses (Tables 2 and 3), so they are not fitted parameters renamed as predictions. The paper does change two hyperparameters when switching to multi-view (pruning threshold scaled by the number of images in 3DGS, and mean noise learning rate divided by the number of images in 3DGS-MCMC, Sec. 10), and the matched-time comparison in Tab. 5 is thin, but these are empirical confounds, not circular reductions. The main unproven link is Proposition 1's assumption that clusters of x coincide with clusters of ∇_w L(x,y); that assumption is stated explicitly rather than derived, making it a correctness or generalization risk rather than a circular step, and it does not force the experimental results by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces a training scheme, not new physical entities. It relies on standard variance-reduction theory and several domain assumptions (gradient clustering by viewpoint, depth reliability). It also adjusts hyperparameters manually (batch size, pruning threshold multiplier, MCMC noise learning rate divisor) when switching to multi-view, as disclosed in Sec. 10.

free parameters (3)
  • Multi-view batch size B = 4
    Number of images rendered per iteration, fixed to 4 across all experiments (Sec. 6). Tab. 6 shows performance grows with B, so this choice is a hand-selected hyperparameter rather than derived from the theory.
  • Pruning threshold multiplier = 4 (equal to B)
    Sec. 10: "the threshold hyperparameter for pruning Gaussians was multiplied by the number of images." Tuned after observing the learning pattern changed under multi-view training.
  • Mean noise learning rate divisor = 4 (equal to B)
    Sec. 10: for 3DGS-MCMC "the mean noise learning rate was divided by the number of images." Hand-tuned compensation for multi-view gradient scale.
assumptions (4)
  • standard math Variance of mini-batch gradients is the key determinant of optimization quality
    Imported from Zhao and Zhang [47]. Accepted stochastic-optimization background.
  • domain assumption Each viewpoint forms a cluster in data space, and data-space clusters coincide with gradient clusters
    Sec. 8.1, Proposition 1: "assume that we have a clustering method... clusters of x are the same as the clusters of psi." This is not established for 3DGS gradients.
  • domain assumption Predicted depth from the rasterizer is accurate enough for unprojecting pixels to 3D for D-SSIM
    Sec. 5.1.2: "A matrix M_pixel_to_world can be obtained using the predicted depth from the rasterizer." There is no analysis of depth-error effects.
  • domain assumption NeRF gradient contributions concentrate at the surface intersection point (Lemma 2)
    Used in Sec. 8.1 to justify Proposition 2 for NeRF; relies on an approximate delta-function treatment of volumetric rendering integrals.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Efficient multi-view training for 3D Gaussian Splatting." pith.science (2026). https://pith.science/paper/5QDO7WVE

@misc{pith2026250612727,
  author       = {Pith},
  title        = {Pith review of: Efficient multi-view training for 3D Gaussian Splatting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5QDO7WVE}},
  note         = {Machine review of arXiv:2506.12727}
}
read the original abstract

3D Gaussian Splatting (3DGS) has emerged as a preferred choice alongside Neural Radiance Fields (NeRF) in inverse rendering due to its superior rendering speed. Currently, the common approach in 3DGS is to utilize "single-view" mini-batch training, where only one image is processed per iteration, in contrast to NeRF's "multi-view" mini-batch training, which leverages multiple images. We observe that such single-view training can lead to suboptimal optimization due to increased variance in mini-batch stochastic gradients, highlighting the necessity for multi-view training. However, implementing multi-view training in 3DGS poses challenges. Simply rendering multiple images per iteration incurs considerable overhead and may result in suboptimal Gaussian densification due to its reliance on single-view assumptions. To address these issues, we modify the rasterization process to minimize the overhead associated with multi-view training and propose a 3D distance-aware D-SSIM loss and multi-view adaptive density control that better suits multi-view scenarios. Our experiments demonstrate that the proposed methods significantly enhance the performance of 3DGS and its variants, freeing 3DGS from the constraints of single-view training.

Figures

Figures reproduced from arXiv: 2506.12727 by the authors.

Figure 1
Figure 1. Comparison between single-view and multi-view training. We observed that single-view training degrades the efficacy of mini-batch training compared to multi-view training for both NeRF and 3DGS. However, the na¨ıve adoption of multi-view training has limitations in terms of performance boost and considerable overhead. Our method realizes efficient multi-view training with reduced overhead, with the same number of Ga… view at source ↗
Figure 2
Figure 2. Variance of mini-batch gradients. The figure indicates that multi-view mini-batch has a smaller variance of mini-batch gradients than single-view, resulting in more stable and effective optimization. 4.1. Mini-batch strategies of NeRF and 3DGS The difference in mini-batch composition between NeRF and 3DGS mainly stems from different rendering schemes in the two methods and affects the stability and efficacy of optim… view at source ↗
Figure 3
Figure 3. Comparison of na¨ıve partial rendering and thread-efficient partial rendering. While the na¨ıve implementation incurs lots of idle threads that do not participate in rendering, our thread-efficient rendering scheme does not generate such idle threads, mitigating the substantial overhead of rendering in multi-view training. Then, it is demonstrated in [47] that mini-batch training is more effective as the variance of… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: 3D distance-aware gaussian filter. For 3D distance￾aware D-SSIM loss, we lift the points from pixel space to 3D space and compute the Gaussian kernel using the 3D distance. apply conventional D-SSIM loss. Therefore, we devise 3D distance-aware D-SSIM loss that can be c…
Figure 5
Figure 5. Figure 5: Invalid arithmetic of gradients in different spaces. Even though both 2D positional gradients derive the same 3D gra￾dients, the addition of those vectors results in the zero vector. Like￾wise, adding 2D positional gradients in different spaces is invalid, leading to u…
Figure 6
Figure 6. Figure 6: Qualitative results of baselines and our model. We provide comparison of 3DGS [12] and 3DGS-MCMC [13]. Our method successfully enhances both models, proving its compatibility with a variety of models. we evaluate our modified densification process for multi￾view traini…
Figure 7
Figure 7. Figure 7: Illustration of the two cases in proposition 1. The figure demonstrates the situation where we draw five samples for each set, composing five sets. Triangles with the same colors in￾dicate samples in the same set. As shown in (a), drawing samples from a single distribu…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

61 extracted references · 47 canonical work pages

  1. [1]

    Gaussian shell maps for efficient 3d human generation

    Rameen Abdal, Wang Yifan, Zifan Shi, Yinghao Xu, Ryan Po, Zhengfei Kuang, Qifeng Chen, Dit-Yan Yeung, and Gor- don Wetzstein. Gaussian shell maps for efficient 3d human generation. In CVPR, 2024. 2

    work page 2024

  2. [2]

    Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P

    Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neu- ral radiance fields. In ICCV, 2021. 7

    work page 2021

  3. [3]

    Barron, Ben Mildenhall, Dor Verbin, Pratul P

    Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In CVPR, 2022. 6, 7

    work page 2022

  4. [4]

    Tensorf: Tensorial radiance fields

    Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In ECCV, 2022. 2

    work page 2022

  5. [5]

    Hallucinated neural radiance fields in the wild

    Xingyu Chen, Qi Zhang, Xiaoyu Li, Yue Chen, Ying Feng, Xuan Wang, and Jue Wang. Hallucinated neural radiance fields in the wild. In CVPR, 2022. 2

    work page 2022

  6. [6]

    K-planes: Explicit radiance fields in space, time, and appearance

    Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, and Angjoo Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In CVPR, 2023. 2

    2023

  7. [7]

    Instruct-nerf2nerf: Edit- ing 3d scenes with instructions

    Ayaan Haque, Matthew Tancik, Alexei Efros, Aleksander Holynski, and Angjoo Kanazawa. Instruct-nerf2nerf: Edit- ing 3d scenes with instructions. In ICCV, 2023. 2

    work page 2023

  8. [8]

    Deep blending for free-viewpoint image-based rendering

    Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow. Deep blending for free-viewpoint image-based rendering. TOG, 2018. 6, 7

    work page 2018

Show all 61 references
  1. [9]

    Barron, Pieter Abbeel, and Ben Poole

    Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object gen- eration with dream fields. In CVPR, 2022. 2

    2022

  2. [10]

    Sdfdiff: Differentiable rendering of signed distance fields for 3d shape optimization

    Yue Jiang, Dantong Ji, Zhizhong Han, and Matthias Zwicker. Sdfdiff: Differentiable rendering of signed distance fields for 3d shape optimization. In CVPR, 2020. 2

    2020

  3. [11]

    Gaussianshader: 3d gaussian splatting with shading functions for reflective surfaces

    Yingwenqi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xiaox- iao Long, Wenping Wang, and Yuexin Ma. Gaussianshader: 3d gaussian splatting with shading functions for reflective surfaces. In CVPR, 2024. 3

    2024

  4. [12]

    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. In TOG, 2023. 1, 2, 6, 7, 8, 15

    2023

  5. [13]

    3d gaussian splatting as markov chain monte carlo

    Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Wei- wei Sun, Jeff Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, and Kwang Moo Yi. 3d gaussian splatting as markov chain monte carlo. In arXiv, 2024. 6, 7, 8, 15

    2024

  6. [14]

    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. TOG, 2017. 6, 7

    2017

  7. [15]

    Decomposing nerf for editing via feature field dis- tillation

    Sosuke Kobayashi, Eiichi Matsumoto, and Vincent Sitz- mann. Decomposing nerf for editing via feature field dis- tillation. In NeurIPS, 2022. 2

    2022

  8. [16]

    WildGaussians: 3D gaussian splatting in the wild

    Jonas Kulhanek, Songyou Peng, Zuzana Kukelova, Marc Pollefeys, and Torsten Sattler. WildGaussians: 3D gaussian splatting in the wild. arXiv, 2024. 3

    2024

  9. [17]

    Dream- scene: 3d gaussian-based text-to-3d scene generation via for- mation pattern sampling

    Haoran Li, Haolin Shi, Wenli Zhang, Wenjun Wu, Yong Liao, Lin Wang, Lik-hang Lee, and Pengyuan Zhou. Dream- scene: 3d gaussian-based text-to-3d scene generation via for- mation pattern sampling. arXiv preprint arXiv:2404.03575,

    arXiv

  10. [18]

    Neural 3d video synthesis from multi-view video

    Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, et al. Neural 3d video synthesis from multi-view video. In CVPR, 2022. 2

    2022

  11. [19]

    Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images

    Yuan Liu, Peng Wang, Cheng Lin, Xiaoxiao Long, Jiepeng Wang, Lingjie Liu, Taku Komura, and Wenping Wang. Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images. In SIGGRAPH, 2023. 2

    2023

  12. [20]

    A large-scale outdoor multi-modal dataset and benchmark for novel view synthesis and implicit scene reconstruction

    Chongshan Lu, Fukun Yin, Xin Chen, Tao Chen, Gang YU, and Jiayuan Fan. A large-scale outdoor multi-modal dataset and benchmark for novel view synthesis and implicit scene reconstruction. In arXiv, 2023. 6, 7

    2023

  13. [21]

    Neural microfacet fields for inverse render- ing

    Alexander Mai, Dor Verbin, Falko Kuester, and Sara Fridovich-Keil. Neural microfacet fields for inverse render- ing. In ICCV, 2023. 2

    2023

  14. [22]

    Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, and Daniel Duck- worth. NeRF in the Wild: Neural Radiance Fields for Un- constrained Photo Collections. In CVPR, 2021. 2

    2021

  15. [23]

    Im-3d: Iterative multiview diffusion and re- construction for high-quality 3d generation

    Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Na- talia Neverova, Andrea Vedaldi, Oran Gafni, and Filippos Kokkinos. Im-3d: Iterative multiview diffusion and re- construction for high-quality 3d generation. arXiv preprint arXiv:2402.08682, 2024. 2

    2024 arXiv

  16. [24]

    Srinivasan, Matthew Tancik, Jonathan T

    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. In ECCV, 2020. 1, 2

    2020

  17. [25]

    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., 2022. 1, 2, 7

    2022

  18. [26]

    Gsedit: Efficient text-guided edit- ing of 3d objects via gaussian splatting

    Francesco Palandra, Andrea Sanchietti, Daniele Baieri, and Emanuele Rodol `a. Gsedit: Efficient text-guided edit- ing of 3d objects via gaussian splatting. arXiv preprint arXiv:2403.05154, 2024. 2

    2024 arXiv

  19. [27]

    Fast dy- namic 3d object generation from a single-view video

    Zijie Pan, Zeyu Yang, Xiatian Zhu, and Li Zhang. Fast dy- namic 3d object generation from a single-view video. arXiv preprint arXiv 2401.08742, 2024. 2

    2024

  20. [28]

    Barron, Sofien Bouaziz, Dan B Goldman, Steven M

    Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In ICCV, 2021. 2

    2021

  21. [29]

    Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin- Brualla, and Steven M

    Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin- Brualla, and Steven M. Seitz. Hypernerf: A higher- dimensional representation for topologically varying neural radiance fields. In TOG, 2021. 2

    2021

  22. [30]

    Dreamgaussian4d: Genera- tive 4d gaussian splatting

    Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, and Ziwei Liu. Dreamgaussian4d: Genera- tive 4d gaussian splatting. arXiv preprint arXiv:2312.17142,

    arXiv

  23. [31]

    Pointpronets: Consolidation of point clouds 9 with convolutional neural networks

    Riccardo Roveri, A Cengiz ¨Oztireli, Ioana Pandele, and Markus Gross. Pointpronets: Consolidation of point clouds 9 with convolutional neural networks. In Computer Graphics Forum, 2018. 2

    2018

  24. [32]

    Plenoxels: Radiance fields without neural networks

    Sara Fridovich-Keil and Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, 2022. 1, 2, 7

    2022

  25. [33]

    Structure- from-motion revisited

    Johannes L Schonberger and Jan-Michael Frahm. Structure- from-motion revisited. In CVPR, 2016. 6

    2016

  26. [34]

    Contrastive gaussian clustering: Weakly supervised 3d scene segmentation

    Myrna C Silva, Mahtab Dahaghin, Matteo Toso, and Alessio Del Bue. Contrastive gaussian clustering: Weakly supervised 3d scene segmentation. arXiv preprint arXiv:2404.12784,

    arXiv

  27. [35]

    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. In TVCG, 2023. 2

    2023

  28. [36]

    Dreamgaussian: Generative gaussian splatting for ef- ficient 3d content creation

    Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for ef- ficient 3d content creation. In ICLR, 2023. 3

    2023

  29. [37]

    Ref-nerf: Struc- tured view-dependent appearance for neural radiance fields

    Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T Barron, and Pratul P Srinivasan. Ref-nerf: Struc- tured view-dependent appearance for neural radiance fields. In CVPR, 2022. 2

    2022

  30. [38]

    Clip-nerf: Text-and-image driven manipula- tion of neural radiance fields

    Can Wang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao. Clip-nerf: Text-and-image driven manipula- tion of neural radiance fields. CVPR, 2022. 2

    2022

  31. [39]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 2004. 5, 6

    2004

  32. [40]

    4d gaussian splatting for real-time dynamic scene rendering

    Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang. 4d gaussian splatting for real-time dynamic scene rendering. In CVPR, 2024. 2, 3

    2024

  33. [41]

    Gaussctrl: multi-view consistent text-driven 3d gaussian splatting edit- ing

    Jing Wu, Jia-Wang Bian, Xinghui Li, Guangrun Wang, Ian Reid, Philip Torr, and Victor Adrian Prisacariu. Gaussctrl: multi-view consistent text-driven 3d gaussian splatting edit- ing. arXiv preprint arXiv:2403.08733, 2024. 2

    2024 arXiv

  34. [42]

    Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision

    Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, and Honglak Lee. Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision. NeurIPS, 2016. 2

    2016

  35. [43]

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

    Zeyu Yang, Hongye Yang, Zijie Pan, and Li Zhang. Real- time photorealistic dynamic scene representation and render- ing with 4d gaussian splatting. In ICLR, 2024. 2, 3

    2024

  36. [44]

    Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models

    Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, and Xinggang Wang. Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models. InCVPR,

  37. [45]

    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

  38. [46]

    Loss functions for image restoration with neural networks

    Hang Zhao, Orazio Gallo, Iuri Frosio, and Jan Kautz. Loss functions for image restoration with neural networks. IEEE Transactions on computational imaging, 2016. 5

    2016

  39. [47]

    Accelerating minibatch stochastic gradient descent using stratified sampling

    Peilin Zhao and Tong Zhang. Accelerating minibatch stochastic gradient descent using stratified sampling. arXiv,

  40. [48]

    Gala3d: Towards text-to-3d complex scene generation via layout-guided generative gaussian splatting

    Xiaoyu Zhou, Xingjian Ran, Yajiao Xiong, Jinlin He, Zhi- wei Lin, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Gala3d: Towards text-to-3d complex scene generation via layout-guided generative gaussian splatting. arXiv preprint arXiv:2402.07207, 2024. 2

    2024 arXiv

  41. [49]

    Tip-editor: An accurate 3d editor fol- lowing both text-prompts and image-prompts

    Jingyu Zhuang, Di Kang, Yan-Pei Cao, Guanbin Li, Liang Lin, and Ying Shan. Tip-editor: An accurate 3d editor fol- lowing both text-prompts and image-prompts. TOG, 2024. 2

    2024

  42. [50]

    Ewa volume splatting

    Matthias Zwicker, Hanspeter Pfister, Jeroen Van Baar, and Markus Gross. Ewa volume splatting. In Proceedings Visualization, 2001. VIS’01., 2001. 1, 2, 3 10

    2001

  43. [51]

    Derivations We provide theoretical derivations of why the multi-view mini-batch strategy is superior to the single-view mini- batch strategy in NeRF and 3DGS in Sec. 8.1. In addi- tion, we derive the situation where the invalid addition of gradients in different image spaces l...

  44. [52]

    Then by Lemma 1, VS1 (Eψ(x,y)∈S [ψ(x, y)])<V S2 (Eψ(x,y)∈S [ψ(x, y)]) (46) Because we assume that clusters ofxare the same as the clusters ofψ(x, y), the proposition holds. Lemma 2.For a single rayr(t) =o+td, the intersection point on the rayr(t i)and weights of NeRFw σ andw c...

  45. [53]

    Additional experiment We compare different numbers of viewpoints in each itera- tion in Tab. 6. We observe that performance increases if we add the number of viewpoints for a mini-batch, but it should be noticed that more viewpoints in a minibatch is equivalent to more memory ...

  46. [54]

    Other hyperparame- ters are the same as before

    Experiment details When applying multi-view training, we found that the learn- ing pattern changed, so in 3DGS, the threshold hyperparam- eter for pruning Gaussians was multiplied by the number of images, and in 3DGS-MCMC, the mean noise learning rate was divided by the number...

  47. [55]

    The 3DGS rasterizer primarily con- sists of two main components: preprocessing and rendering

    Rasterization in multi-view training We describe the modifications made to the 3DGS rasterizer for multi-view training. The 3DGS rasterizer primarily con- sists of two main components: preprocessing and rendering. In the preprocessing stage, the rasterizer performs the fol- lo...

  48. [56]

    Computing projected 2D Gaussians

  49. [57]

    Duplicating projected Gaussians for each tile they cover

  50. [58]

    First, to reduce memory consumption, the ras- terizer determines which Gaussians participate in render- ing for each viewpoint in a mini-batch before preprocess- ing

    Sorting Gaussians by depth for each tile For multi-view training, we introduce the following mod- ifications. First, to reduce memory consumption, the ras- terizer determines which Gaussians participate in render- ing for each viewpoint in a mini-batch before preprocess- ing. ...

  51. [59]

    It is necessary to eliminate the overhead of the preprocessing step to achieve constant time complexity concerning the number of images as NeRF

    Limitations Even though we successfully mitigate the overhead of ren- dering part in rasterization, the overhead caused by the pre- processing step still remains. It is necessary to eliminate the overhead of the preprocessing step to achieve constant time complexity concerning...

  52. [60]

    Detailed results We provided full results of the main table in Tab. 7

  53. [61]

    We report the full table of 3DGS [12], 3DGS-MCMC [13] and the results of adding our method to them

    Algorithms We provide detailed pseudocodes of the difference between the mini-batch strategy of NeRF and 3DGS in Algorithm 1, 14 3DGS 3DGS + Ours MCMC MCMC + Ours PSNR↑/ SSIM↑/ LPIPS↓PSNR↑/ SSIM↑/ LPIPS↓ PSNR↑/ SSIM↑/ LPIPS↓PSNR↑/ SSIM↑/ LPIPS↓ MipNeRF-360 Bicycle 25.62 / 0.77...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.