A^2TG: Adaptive Anisotropic Textured Gaussians for Efficient 3D Scene Representation
Authors: Sheng-Chi Hsu
Ting-Yu Yen
Shih-Hsuan Hung
Hung-Kuo Chu
Venue: ICLR 2026 Poster (2026)
Keywords: Computer Vision;Novel View Synthesis:Neural Rendering;3D Gaussian Splatting
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Gaussian Splatting has emerged as a powerful representation for high-quality, real-time 3D scene rendering. While recent works extend Gaussians with learnable textures to enrich visual appearance, existing approaches allocate a fixed square texture per primitive, leading to inefficient memory usage and limited adaptability to scene variability. In this paper, we introduce adaptive anisotropic textured Gaussians (A$^2$TG), a novel representation that generalizes textured Gaussians by equipping each primitive with an anisotropic texture. Our method employs a gradient-guided adaptive rule to jointly determine texture resolution and aspect ratio, enabling non-uniform, detail-aware allocation that aligns with the anisotropic nature of Gaussian splats. This design significantly improves texture efficiency, reducing memory consumption while enhancing image quality. Experiments on multiple benchmark datasets demonstrate that A$^2$TG consistently outperforms fixed-texture Gaussian Splatting methods, achieving comparable rendering fidelity with substantially lower memory requirements.

Published as a conference paper at ICLR 2026
A TT ADAPTIVE ANISOTROPIC TEXTURED GAUS- SIANS FOR EFFICIENT 3D SCENE REPRESENTATION
Sheng-Chi Hsu
Ting-Yu Yu Yen
Shih-Hsuan Hung
Hung-Kuo Chu
National Tsing Hua University
ABSTRACT
Gaussian Splatting has emerged as powerful representation for high-quality, real- time 3D scene rendering. While recent works extend Gaussians with learnable textures to enrich visual appearance, existing approaches allocate fixed square texture per primitive, leading to inefficient memory usage and limited adaptabil- ity to scene variability. In this paper, we introduce adaptive anisotropic tex- tured Gaussians (A²TG), a novel representation that generalizes textured Gaus- sians by equipping each primitive with an anisotropic texture. Our method em- ploys a gradient-guided adaptive rule to jointly determine texture resolution and aspect ratio, enabling non-uniform, detail-aware allocation that aligns with the anisotropic nature of Gaussian splats. This design significantly improves texture efficiency, reducing memory consumption while enhancing image quality. Experi- ments on multiple benchmark datasets demonstrate that TTG consistently outper- forms fixed-texture Gaussian Splatting methods, achieving comparable rendering fidelity with substantially lower memory requirements.
INTRODUCTION
Recent advances in Gaussian Splatting have established it as powerful paradigm for 3D scene representation, offering both high-fidelity reconstructions and real-time rendering performance. In particular, 3D Gaussian Splatting (3DGS) and its 2D variant, 2D Gaussian Splatting (2DGS), have attracted significant attention due to their ability to combine explicit geometric structure with effi- cient differentiable rasterization, enabling compelling results across range of applications in view synthesis, reconstruction, and immersive content creation.
Unlike mesh- based representations, which compactly encode repetitive fighffrequecyy appearance via texture maps, Gaussian Splatting typically approximates such detail by instantiating many small primitives, leading artifacts and inefficient memory usage. Chao al. (2025) show that attaching a learnable, uniform square texture patch to each Gaussian substantially enhances visual richness and realism. However, this design ignores variability in primitive geometry and opacity. Some Gaussians require high-frequency textures to capture fine detail, whereas others only need to convey coarse appearance with simple texture. Consequently, fixed-size texture allocations waste memory, inflate storage, and poorly adapt to the anisotropic Gaussians.
In this paper, we propose adaptive anisotropic textured Gaussians (A (A²TG), a novel representation designed to address these limitations. Instead of attaching a uniform square texture to each Gaussian, TT assigns an anisotropic texture whose resolution and aspect ratio are jointly determined by gradient-based adaptive texture control strategy. This adaptive anisotropic texture allows Gaussians to have different patterns and details across the scene. Gaussians covering high-frequency or direc- tionally elongated regions are assigned higher-resolution anisotropic textures, while simpler regions receive more compact representations. However, it challenging to devise a principle to guide per- Gaussian texture resolution and pixel values. Many 2D Gaussians yield little benefit from textures due to small projected footprints, low opacity, and occlusion. We introduce gradient-guided adap- tive control to iteratively upscale the textures according the positional gradient and the geometry

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Published as a conference paper at ICLR 2026
of the Gaussians. As result, TG improves memory efficiency while also achieving comparable image quality, as texture parameters are more efficiently allocated.
We evaluate A across diverse datasets and benchmark tasks. Our results show that the pro- posed approach achieves more effective texture utilization, leading to comparable image quality compared to textured Gaussian Splatting methods with fixed square textures, while substantially ducing memory overhead. These findings suggest that our adaptive anisotropic texture allocation is a promising path toward scalable and efficient textured Gaussian representations.
Our main contributions are summarized as follows.
We introduce A TGG a generalization of Textured Gaussians that leverages an adaptive anisotropic texture for each Gaussian.
We propose gradient- based adaptive texture control strategy for allocating texture resolu- tion and aspect ratio, enabling non-uniform, detail-aware texture mapping.
We demonstrate that A²TG improves memory efficiency while maintaining comparable visual fidelity across multiple benchmarks, advancing the state-of-the-art in texture-based Gaussian Splatting.
RELATED WORK
2.1
NOVEL VIEW SYNTHESIS
Novel View Synthesis addresses the challenge of generating realistic renderings of 3D scenes from previously unseen viewpoints, given a set of captured images with known camera poses (Chaurasia) et al. 2013 Hedman et al.. 2018b; Kopanas et al. (021). Neural Radiance Fields (NeRF) represent scenes using a multi-layer perceptron (MLP) that models both geometry and view-dependent ap- pearance, trained through volume rendering to achieve high-fidelity image synthesis (Barron al.
Tancik Tancikk et al. 2020 Mildenhall et al. 202T) On the other hand, 3D Gaussian Splatting (3DGS)
(Kerbl al., (023) has emerged a powerful alternative, capable of delivering real-time novel view synthesis with impressive visual quality. Building on its success, extensions of 3DGS have rapidly appeared across diverse applications. More recently, 2D Gaussian Splatting (2DGS) (Huang al.) 2024) builds upon 3DGS by introducing a "flattened" variant of Gaussian primitives that aligns more naturally with object surfaces, allowing more accurate intersection calculation and opening the door to further applications such as mesh extraction.
2.2 TEXTURED GAUSSIAN SPLATTING REPRESENTATION
The color of each Gaussian primitive is typically parameterized by spherical-harmonic (SH) coef- ficients, which, given a viewing direction, evaluate to a view-dependent RGB. Consequently, for fixed view, each primitive yields single color distributing over its 2D footprint, limiting intra- primitive color detail. Huang & Gong (022)) adds spatially varying appearance to each 3D Gaus- sian, enabling richer 3DGS representation. Texture-GS (Xu et al. 20246) maps textures onto 3D Gaussians. but the method is demonstrated primarily on small objects, limiting scalability. Chao
al. (2025) further introduces RGBA texture maps to improve visual quality.
By contrast, 2DGS (Huang et al. 2024 conforms more closely to underlying surfaces and provides a more accurate parameterization for texture mapping, making it better suited for texture integration (Svitov et al., 2024,) Xu et al. 2024a: Weiss & Bradley, 2024)) Rong et al. 2025| [Song et al. 2024). These methods employ predefined RGB or RGBA textures, and (Rong et al. 2025) further adopts world-space texture mapping. However, these methods assign fixed-size and square texture to every 2D Gaussian. Because Gaussians have varying geometry and opacity, uniform texture sizes are -nefffiinnfficcien;; tiny highly transparent Gaussians gain little benefit while inflating storage. Thus, we propose A'GG, which uses adaptive anisotropic textured Gaussians that allocate texture resolution each primitive according its footprint and gradient.
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2.3
MEMORY- EFFICIEET GAUSSIAN SPLATTING REPRESENTATION
Gaussian primitives offer efficient real-time rendering via learned positions, covariances, color, and opacity; however, 3DGS models are typically storage-intensive due to the large number of primi- tives and per-primitive attributes. Several 3DGS compression methods address this issue, including vector quantization (Fan et al. 2024:
Lee et al. 2024; Niedermayr et al.) 2024). and splat-count reduction (Papantonakis et al. 2024 Zhang et al. 2024 Mallick et al., 2024), and context mod-
els (Wang al., 2024) These approaches can improve memory efficiency, while the results have similar visual fidelity to the original Gaussian models (Bagdasarian et al.) (025). In this work, we target memory-efficiet textured Gaussians by adaptively controlling per-Gaussian texture resolu- tion; this direction is orthogonal and complementary to prior compression methods and remains compatible with them.
3 PRELIMINARIES
Textured Gaussian splatting methods naturally build upon 2D Gaussian splatting (Huang et al,, 2024), , as the latter provides convenient framework for defining ue-coordinates in the local space of 2D splats, thereby enabling consistent texture sampling.
More concretely, 2D Gaussian Splatting represents 3D scene using collection of flat 2D Gaus- sians instead of volumetric 3D Gaussians (Kerbl et al. 2023). Each Gaussian is parameterized as
(14 St, Oz, c.
where μr denotes its center position, s; the 2D scale, quaternion rotation, the opacity, and ci the spherical harmonics coefficients encoding view-dependent appearance.
For each 2D Gaussian, a local uu-coordinates system is defined. The mapping from this local space the screen space expressed as
(1) where (z. y, 1, the homogeneous ray passing through pixel (a., y). (u, v, T)T is the corresponding intersection point in the local Gaussian space, W E R4x4 is the world-to-screen transformation matrix, and is the to-l-ld-woorld transformation constructed from the Gaussian's position, rotation, and scale (Huang al. 2024).
X WH u. v, T,
To render a set of Gaussians, w--coordinates of the ray-splat intersection are first computed as u(x) v()) by inverting Equation ! The final color of each pixel is then obtained using front-to-back alpha compositing:
1-1 (1 - o1-,))((u()))) (u()))))
c(x) = E.oG(((())
II ==1
where c; is the color of the i-th Gaussian and Gi is its Gaussian kernel evaluated at the uv- coordinates.
4 METHODOLOGY
Recent works augment 2D Gaussians with texture maps to improve rendering quality and efficiency. Most assign a fixed, square texture to every Gaussian, regardless of its scale, opacity, or visibil- ity. This creates redundancy and unnecessary memory usage. We instead assign each 2D Gaussian an individual texture resolution and shape, thereby allocating parameters where they matter most. Our framework starts with training the given scene by integrating Markov Chain Monte Carlo (MCMC) densification scheme (Kheradmand et al.] (024) into the 2DGS pipeline (Huang et al.) 2024 for the first 30, 000 iterations. With MCMC densification, we can easily control the total number of Gaussians in the final results. During this pretraining stage, each Gaussian's texture is (i.e., without textures). Next, we fix the total number of 2D Gaussians, set the texture alpha A 1. and train the 2DGS model with the gradient-basse based adaptive texture control for another 30, 000 iterations. At each iteration,
fixed to 1 1 with texture color RCB and texture alpha T
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By
P-M
f >> 82
18 R 8
T +27
T
(6) Opimization
(b) Giradient-Divvee Driven Gaussian
Adeptive Texture Upscaling
Selection
Overview gradient-based adaptive texture control. Given an initial 2DGS model, (a) our system first optimizes the parameters of the 2D Gaussians and their textures. (b) Next, we compute the positional gradient of the textured 2D Gaussians (as depicted in gray blocks) and select the 2D Gaussians that need to increase the resolution of the texture to capture more details. (c) Finally, we adaptively upscale the textures according to the anisotropy of the Gaussians.
Figure 1:
first update the parameters of the 2D Gaussians and their textures (i.e., TRGB and TA followed by applying gradient- adaptive texture control, which consists of gradient-driver Gaussian selection and adaptive texture upscaling illustrated in Figure [ This procedure adjusts the reso- lution and aspect ratio of each texture to match the anisotropic footprint of its Gaussian, upscaling where gradients are large. We detail the gradient-based adaptive texture control the following subsection.
4.1 ANISOTROPIC TEXTURED GAUSSIANS
We first augment each 2D Gaussian an RGB texture 7 TGGB and an alpha texture T:A, then we utilize the local space of the 2D Gaussians for texture mapping with RGBA textures, as the prior works of textured Gaussians (Xu et al. 2024al Svitov et al. 2024: Chao et al. 2025). After getting the uu-coordinates by taking the inverse of Equation 1 rescale the range of and from [-1, 1] to [0, T.") and [0. 7.%, where TH. and T," are the texture width and height of the i-th 2D Gaussian, so every texture may have different width and height.
The color contribution of the i-th 2D Gaussian to pixel x is now calculated by combining the RGB color from the spherical harmonic caS and the texture color
cx(x) = SH + T.RGB (u(x)),
where maps the pixel x to the uo-coordinates. The alpha value of the i-th Gaussian is calculated by
(4) where G and o; are the Gaussian distribution function and the opacity of the i-th 2D Gaussian. For querying the texture values T.RBB and T.A from un-coordinates u(x), we use bilinear interpolation, following (Chao et al. 2025). Finally, the k-th channel of the resulting pixel value pixel x is calculated on the sorted 2D Gaussians from front to back as
a,(x) o; - ((((x) TAA (u(x)),
c* (x)
(x)asxx II a1- ax(x),))), 1=1
where of is the k-th channel color of the i-th Gaussian. We next utilize the pixel and alpha values to compute the gradient and guide the upscaling of the textures.
4.2 GRADIENT-DRIVER GAUSSIAN SELECTION
It's difficult to design principle to determine the needs of resolution and pixel values of textures for each 2D Gaussian. First, some 2D Gaussians gain little from textures because their projected footprint covers only few pixels in the training views or their opacity is very low. Second, occlusion by foreground splats limits the utility of textures even when Gaussian's 's opacity high. Finally, in regions with homogeneous appearance (e.g.. uniformly colored walls), textures provide minimal benefit regardless of Gaussian size or opacity. Therefore, we introduce the gradient- driven Gaussian selection, similar the densification procedure from 3DGS (Kerbl al. 2023
Ye al. 2024).
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Given rendered view of the 2DGS and its corresponding training view, let L be the L1 and SSIM loss between the views at pixel x and μi = (pi.z Pl.ys (Aa,s) be the position of the i-th Gaussian. The positional gradient from the pixel at coordinate with respect to the i-th Gaussian is calculated
ac ac ac as Wn,C = 0pg.. BP 8ps.s value of the i-th Gaussian
We then derive the ac with the pixel value and the alpha
0ps.
0C Dpi,e
3
Dc dc da: dc
(6)
da: Ous,
The first term corresponds to our idea of using pixel differences to guide texture upscaling, which eliminates the possibility of introducing additional parameters on already reeosreconstruuted regions of the scene. The last two terms involved in the positional gradient calculation contain the informa- tion of occlusion from other Gaussians, the opacity, and the pixel contribution count. They can be computed as
Owp C Das
II a1) [=1
Σ p=i+1
dag
where
Gulp Za:
ap a1), and
dag
(g)
8pi,
0,
0pi,
TAAAA (u).
(8)
Sp..." For the i-th Gaussian, we then ac- cumulate the absolute value of its positional gradient over the covered pixels VurCo C, follow- ing AbsGS(Ye et al.] (024).. the magnitude of the accumulated gradient exceeds threshold (i.e.. Vu, Cll2 kG). it indicates the presence of high-ffree frggufireuuncy content the Gaussian. We then select the Gaussian as a candidate for adaptive texture upscaling.
Similarly, we can derive the equations for ac and ac
ōpty
4.3 ADAPTIVE TEXTURE UPSCALING
After selecting candidate 2D) Gaussians, we adaptively increase their texture resolution based on anisotropy. Specifically, we compute the ratio of the two semi-axes, and Sy, of each 2D Gaussian and use to determine the anisotropy. The texture resolutions T and T are then updated according to the following rules:
If f2//// > KA and Sy < ks, then we double T
If /g////2 KA and 82 < ks. then we double T
Otherwise, we double the resolution of both T and T
Here, kA and Kg are predefined thresholds. This adaptive strategy allocates resolution more effec- tively: textures refined preferentially along the dimension most sensitive gradient variations, while still allowing isotropic refinement when Gaussians are approximately square or both axes require higher detail.
When upscaling, the new texture color TRGB and texture alpha T are initialized from the nearest pixel of the previous texture. Both RRGB and A together with the Gaussian parameters, are then optimized in subsequent iterations. The adaptive texture upscaling is applied every 500 iterations, allowing gradients accumulate before resolution adjustments.
EXPERIMENTS
EXPERIMENTAL SETUP
Datasets and metrics. We conducted experiments the Mip-NeRF 360 dataset (Barron et al. (7 scenes), the Tanks and Temples dataset (Knapitsch et al., 2017) scenes), and the Deep Blending dataset (Hedman al. (2 scenes). The evaluation metries include PSNR, SSIM,
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Mip
Tanks and Temples
DespWlending
Method
PSNK
SSIM
LPIPS 1
Mcm
SSIM 1 LPIPS. :
Men
PSNR 1
SSIM 7 LPIPS
MDCS
28.30
0.836
0.177
200
23.16
0.827
0.151
8671
200,00MB
29.63
0.398
0.192
200 COMB
Super Gauassans
28.31
0.839
0.232
675k
199.5MB
23.49
0.841
0.193
674k
199,89MB
29.52
00.00
0.260
67%k
199 72MU
BBSplat
26.72
0.792
0.257
40k
20000M8
22.51
0.794
0.241
46k
200.00MB
28.73
0.201
0.275
46k
200
Textured Canssiaas
28.37
0.832
0.188
410K
20000011
23.41
0.824
0.164
410k
200,00MB
29.51
0.857
0.198
410k
200-000H
A'IG (Ours)
0.838
0.174
19948818
23.56
0.828
0.153
199,51MB
0.187
189442IB5
2DGS*
26.72
0.787
0.308
25%
600001
22.52
0.791
0.275
210k
50,00MB
28.89
0.539
0.302
25%
60.00MB
2DGSS-MCMM
27.46
0.306
0.228
25%
6100MB
22.55
0.805
0.199
216k
50.00MB
29:24
01.003
0.217
25%K
60.00M
Super Goessians
25.29
0.791
0.300
202K
59 59.9M1
21.46
0.783
0.273
169k
49. 49.52MB
29.01
0,2555
0.292
203
59.9KML
BRSplat
23.07
0.614
0.462
14k
60.00MBB
16.21
0.522
0.579
17k
50,00MB
26
0.833
0.385
14k
60 DOMB
Textured Gaussions
27.33
0.795
0.253
123k
60,0MB
22.60
0.789
0.231
50,00MB
29.19
0.229
60,00MB
A (Ours
27.47
0.305
0:233
58,02MB8
22.73
0.798
0.213
49,09MB
29:42
0.215
59.55MB
Table 1: Quantitative comparison under fixed memory budget. We report PSNR 1. SSIM 1,
LPIPS J, number Gaussians (#GS), and memory (Mem, MB). The top three results are highlighted in red orange and yellow and the least memory sizes are in bold
LPIPS, the number of Gaussians, and the memory size of trainable parameters for each algorithm. The testing set was constructed by selecting every eighth image, while the remaining images were used for training.
Baselines. We compare TT against 2DGS (Huang et al. 2024), and textured Gaussian methods including Textured Gaussians (Chao et al. 2025), BBSplat (Svitov et al. 2024), and SuperGaus- sians (u et al. 2024a).
To ensure a fair comparison, we disable the depth-distortion and normal-consistency losses in 2DGS, as these terms primarily improve mesh quality at the cost of slightly reduced image fidelity. Further- more, since both Textured Gaussians and A²TG are built 2DGS with an MCMC-based density control, we report results for two 2DGS variants: the original 2DGS with the adaptive density con- trol of Kerbl et al. (2023) and 2DGS equipped with the same MCMC strategy Kheradmand al., 2024).
We use unofficial implementation of Textured Gaussians that employs 2DGS for rasterization instead of 3DGS. We denote the modified baselines as 2DGS 2DGS -MCMC and Textured Gaus- sians* respectively.
Implementation details. Textured Gaussians* also adopts a two-stage training process, beginning with an MCMC pretraining. For fairness, all experiments with Textured Gaussians* and our A model share the same first-stage results when trained with the same number of Gaussians. In the second stage, Textured Gaussians use a fixed texture resolution of 4 x 4, while our A2TG method applies adaptive texture upscaling at iterations 500 and 1000. As result, final textures in Textured Gaussians remain 4 x whereas those in A2 vary within f1, 43 21, 2, 4), including both square and anisotropic shapes. The parameters of A are set as hA = 4.0, ks = 0.01, kg 0.00002.
5.2 COMPARISONS
We employ two different comparison settings for a comprehensive evaluation on the performance of our method, one with fixed memory budget, and the other with fixed number of Gaussians. Metries including PSNR, SSIM, LPIPS on all the datasets are reported separately. Note that BBSplat is designed for lower number of Gaussians because of their extra parameters; thus, they were not evaluated under the setting of 500k and 1M Gaussians.
Comparison under a fixed memory budget. We report the visual quality metrics, number of Gaussians, and memory consumption of trainable parameters of all the baselines and our T method in Table I Our method has achieved the best performance across various textured-based 2DGS works under the same memory constraint, demonstrating the efficiency of our method. Since our adaptive texture control is based on gradients threshold selection, we cannot achieve the exact same memory consumption as other methods, but our A2TG method still achieves higher overall visual quality with less memory especially for PSNR. Other texture-basse based methods suffer from excessive texture parameters, thus resulting in worse visual quality under a fixed memory constraint.

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Published as a conference paper at ICLR 2026
Mip-NeRF
Tanks Temples
DeepBlending
Method
PSNR 1 SSIM
LPIPS
Mem(MB,+3)
PSNR 1 SSIM ? LPIPS McmLMB
PSNR SSIM
1 LPIPS 1 +%)
4GS IM
2DGS*
29.58
0.899
0.263
232 (0%)
2DGS-MCMC
0.838
232(009)
232 (0%)
0.190
Super Gaussian
28.39
0.848
0.118
296 (2856)
23.66
0.847
0.185
284 (225)
29.38
0.905
0.253
295 (27%)
Textured Coussianc*
28.81
0.846
0.159
488 (1 (1195)
23.
0.833
0.141
488 10%)
29.64
0.897
0.188
48% (1105)
ATG (Ours)
0.844
0.163
291 (25%))
23.58
0.831
0.145
292 (2666)
29.82
0.900
0.180
(198)
AGS = 5008
2DGS*
27.77
0.811
0.270
116 (0%)
23.08
0.824
0.220
116 (0%)
29.45
0.894
0.279
116 (0%)
MDGG
0.824
0.196
116
22.87
116
0.202
116-00%)
Super Ganssians
28.01
0.831
0.245
148 (28%))
23.31
(2893)
29.49
0.903
01.36
148 (28%)
Tixtured Caussians
28.47
0.836
0.181
(11()
23.50
0.826
0.158
244 (110%)
29.60
0.898
0.195
244 (1105))
ATTG (Ours)
28.31
0.832
0.187
148 (28%)
23.43
0.824
0.163
150 (29%)
29.71
0.899
0.193
140 (21%)
AGS - 700M
20XIS*
19.17
0.734
0.334
23 (0%)
28.32
0.879
01324
23 (0%)
2DGS*
26.40
0.767
0.294
23 (0%)
22.07
0.776
0.245
23 (0%))
28.69
0.883
0.255
(3%)
Super Gaussians
23.12
0.737
0.366
(289))))
20.11
0.737
0.327
(28%)
26.61
0.875
0.331
30 (2898)
BRSplatt
27.73
0,828
0.216
412 (17648))
23:40
0.842
0.164
452 (1 (17661)))))
28.78
0.894
(12668
412. (176645))
Textured Gonesians*
27.04
0.785
0.268
49 (1108)
22.52
0.787
0.233
49 (1105))
29.18
0.890
0.236
49 (110%)
APTG (Ours)
0.775
22.37
(325)
22.22)))
Table 2: Quantitative comparison under a fixed number of Gaussians. We report PSNR 1, SSIM t. L.IPS J. #GS, and memory (MB). The parameters increase for texture parameters relative to the size of 2DGS is added as percentage after memory (MB). The top three results are highlighted in red orange and yellow and the least parameter increases are in bold
For visual comparison, we present the testing-vier view results of TG alongside 2DGS* and Textured Gaussians * in Figure 3 Owing to the flexibility of textured representations, both Textured Gaus- * and A2TG achieve notably higher visual quality than 2DGS* despite the latter using larger number of Gaussians. Moreover, under equal memory budgets, A2TG produces higher-fidelity ren- derings than Textured Gaussians primarily because it can represent the scene with a larger set of Gaussians.
Comparison under a fixed number of Gaussians. We report the visual quality metrics and the memory consumption in [2 The percentage increase in memory is measured relative to 2DGS* with the number of Gaussians fixed to 1M, 500k and 100k. All methods outperform 2DGS due to the textures. BBSplat and Textured Gaussians* typically have slightly better visual quality than our method but require substantially more memory because of the inefficient ways to use the texture parameters. In particular, Textured Gaussians* takes up to more than four times texture memory than A2TG with around 0.4 dB increase in PSNR. We also report the full experiments in Appendix A.
To better illustrate the efficiency of our method, we compare our TT, Textured Gaussians and 2DGS* for different numbers of Gaussians, and plot the relation between memory and PSNR, well the relation between point count and memory as shown in Figure 2 Overall, our method not only has a better reconstruction quality under same memory constraint, but also with less memory usage than Textured Gaussians under the same number of Gaussians.
Distribution of upscaled textures. To illustrate TTG''s efficiency on a complex scene, we color- code Gaussians: blue for 2 x 2 4 x 4 textures, red for non-square textures, and the original color for 1 x (no upscaling). Figure 4 (right) shows the Garden scene from the Mip-NeRF 360 dataset, while Figure (left) plots the percentage distribution of all the texture resolutions. Notably, 62.4% of Gaussians retain x 1 textures under our gradient-driven Gaussian selection, indicating that texture resolution is allocated sparingly where additional detail is unnecessary. Red highlights concentrate along sharp edges (e.g., gaps between table planks), showing that A2TG detects thin anisotropic Gaussians and assigns non-square textures accordingly.
Texture Decomposition. To better understand the role of adaptive textures in A we perform qualitative ablation by selectively disabling components of the appearance model. Figure shows three versions of each scene: (1) the full A rendering, (2) a version without textures, and (3) a version without base color. For the "no textures" variant, we set all RGB textures RRGB to zero and all alpha textures A to one, removing texture-driven modulation. For the "no base color" variant, we set the spherical harmonics color ci to zero, leaving appearance determined solely by the learned textures.
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4MCMC
A (Ours)
Textured Gaussians"
Deep Blending
Deep Blending
PSNR
28.3
100
100
200
300
400
5000X
500K
10008
Memory Size (MB)
Point Count
Figure 2: Comparison of 2DGS, A TG and Textured Gaussians on the DeepBlending datasets. Left: PSNR versus memory size (MB). Right: memory size (MB) versus point count. A achieves higher reconstruction quality under the same memory budget and requires less memory than Textured Gaussians for the same number of Gaussians.
(a) GT
(b) 2DGS*
Textured Gaussians (d) T
Figure 3: Qualitative comparisons. We show the qualitative comparisons of 2DGS* and Textured Gaussians* with A2TG under fixed memory constraint from the Mip-NeRF360 datasets and DeepBlending datasets. With textures, both Textured Gaussians* and A2TG reconstruct fine scene details, whereas AT uses less memory.
As shown Figure 5 removing textures leads to clear loss of high-frequency detail-e.ge leaf boundaries, grass structure, and fine variations in carpets and fabries-indicating that adaptive tex- tures capture cies-consistent residual appearance beyond what low-order spherical harmonics can express. Since every Gaussian contains at least x 1 texture, removing textures affects all regions.
Conversely, removing the SH base color produces overly dark renderings with missing global shad- ing. Although textures recover much of the fine detail, the absence of SH color removes low- frequency illumination and structural coherence, showing that the SH term provides a smooth, lighting-aware foundation.
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square
Percentage
10.0%
0.9%
Texture Shape (I
Texture Resolution Percentage
Texture Resolution Distribution
Figure 4: The percentage and distribution. This figure shows the percentage and distribution of texture resolution produced by the adaptive texture upscaling on the scene Garden from Mip- Nerf360 dataset. Gaussians highlighted in blue have square texture of 2 x 2 and 4, and those highlighted in red have non-square texture resolution.
(a) Full
(b) SH only
(c) Texture only
Figure 5: Qualitative visualization of what the adaptive textures learn. Left: full rendering from T Middle: rendering without textures (RGB textures
one). zero, alpha textures Right: rendering without SH base color. The comparison, visualized on two scenes shows that textures capture high-frequency residual appearance such foliage structure and fabric detail, while SH color provides smooth, low-frequency shading. Together, they produce the final photorealistic result.
Overall, these ablations highlight the complementary roles SH color and adaptive textures: models coarse shading and global appearance, while textures encode local, high-frequency details. This further supports our choice allocating texture resolution adaptively so that texture capacity focused where fine detail needed.
5.3 ABLATION STUDIES
We examine the impact of resolution scaling and anisotropy on our adaptive texture upscaling. Ab- lations are run on three datasets with the number Gaussians fixed at 50k, 100k. 500k and 1M, comparing ATG to two variants: (i) without texture upscaling (w/o Upscaling) and (ii) without anisotropic texture upscaling (w/o Anisotropy). Table 3 reports average memory usage and recon- struction quality (PSNR, SSIM, LPIPS). Removing scaling yields the lowest memory but the worst quality. Disabling anisotropy produces quality close to the full method but uses more memory be- cause square textures cannot match anisotropic Gaussians efficiently.

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Published as a conference paper at ICLR 2026
Method
PSNR1 SSIM LPIPS., Mem
Method
PSNRÍ SSIM LPIPS Mem.
#GS IM
#GS 100k
w/o Upscaling
27.03
0.854
0.170
232.00
w/o Upscaling
25.72
0.809
0.264
23.20
w/o Anisotropy
27.38
0.859
0.162
298.22
w/o Anisotropy
26.01
0.815
0.257
31.10
Ours
27.37
0.858
0.163
286.54
Ours
26.01
0.814
0.256
29.67
#GS 500k
#GS 50k
w/o Upscaling
26.76
0.846
0.188
116.00
w/o Upscaling
24.87
0.780
0.318
11.60
w/o Anisotropy
27.19
0.852
0.181
152.10
w/o Anisotropy
25.19
0.787
0.308
15.51
Ours
27.15
0.852
0.181
145.92
Ours
25.17
0.786
0.309
14.73
Table 3: Ablation study with varying numbers of Gaussians. We report PSNR (1), SSIM (t),
LPIPS (J), and memory usage in MB (L), averaged across Mip-NeRF 360, Tanks& Temples, and DeepBlending. The top three results in each column are highlighted in red orange and yellow
CONCLUSION
Textured Gaussians enhance visual quality by augmenting each Gaussian with RGB texture map for spatially varying color and an alpha texture for spatially varying opacity, but these methods typ- ically introduce too many parameters and causes memory inefficiency. To address this, propose adaptive anisotropic textured Gaussians (A²TG) that allows rectangular texture maps whose aspect ratios and resolutions aligned with the geometry of the Gaussians. This reduces memory storage while preserving fine details. We also introduce a gradient- based adaptive texture control strategy to efficiently determine the texture shapes along with parameter update for joint optimization. TG has achieved best visual quality among state-of-the-art methods under the same memory constraint, demonstrating efficiency of our method.
Limitation and future work. We demonstrate that A produces efficient textured Gaussians with better visual quality and memory efficiency. Currently, we only upscale texture resolution of Gaussians. To further improve the efficiency of texture usage, we can scale down the texture resolution when necessary. Moreover, compressing textures and controlling the shape and size of Gaussians can also improve memory efficiency. For future work, combining adaptive textures with flexible primitives beyond 2D/3D Gaussians such as Deformable Radial Kernel Splatting(Huang al. 2025) might further enable better texture utilization since those primitives have flexible and sharper boundary shapes. addition, we also plan to develop dynamic GS models, such as 4DGS, using TTG.
ACKNOWLEDGEMMETS
The authors wish thank our anonymous reviewers for their constructive feedback. The project was funded in part by the National Science and Technology Council of Taiwan (114-2221-F-007. 114-MY3, 113-222 114-222-
REFERENCES
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Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin Sirtin--ruuala,, and Pratul P Srinivasan. Mip-NeRF: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 5855- 5864, 2021.
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Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-NeRF 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 5470-5479, 2022.
Brian Chao, Hung- Yu Tseng, Lorenzo Porzi, Chen Gao, Tuotuo Li, Qinbo Li, Ayush Saraf, Jia-Bin Huang, Johannes Kopf, Gordon Wetzstein, et al. Textured gaussians for enhanced 3d scene ap- pearance modeling. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 8964-89744 8974, 2025.
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Zhentao Huang and Minglun Gong. Textured-GS: Gaussian splatting with spatially defined color and opacity. arXiv preprint arXiv:2407.09)) 09733, 2024.
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splat- ting for real-time radiance field rendering. ACM Trans. Graph., 1,
Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Yang-Che Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, and Kwang Moo Yi. 3d gaussian splatting as markov chain monte carlo. Advances in Neural Information Processing Systems, 37:80955-80988 2024.
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.
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Saswat Subhajyoti Mallick, Rahul Goel, Bernhard Kerbl, Markus Steinberger, Francisco Vicente Carrasco, and Fernando De La Torre. TTaming 3DGS: High-quality radiance fields with limited resources. In SIGGRAPH Asia 2024 Conference Papers, pp. 1-11, 2024.
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF; Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1)999-106, 2021.
Simon Niedermayr, Josef Stumpfegger, and Rüdiger Westermann. Compressed 3d gaussian splatting for accelerated novel view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 00349-10358 2024.

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Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, and George Dret- takis. Reducing the memory footprint of 3d gaussian splatting. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 7(1):1-17, 2024.
Victor Rong, Jingxiang Chen, Sherwin Bahmani, Kiriakos N Kutulakos, and David B Lindell. GS- tex: Per-primitive texturing of 2d gaussian splatting for decoupled appearance and geometry mod- eling. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp- 1508-3518. IEEE, 2025.
Yunzhou Song, Heguang Lin, Jiahui Lei, Lingjie Liu, and Kostas Daniilidis. Hdgs: Textured 2d gaussian splatting for enhanced scene rendering. arXiv preprint urXiv:2412.01823, 2024.
David Svitov, Pietro Morerio, Lourdes Agapito, and Alessio Del Bue. BillBoard Splatting (BB- Splat): Learnable textured primitives for novel view synthesis. arXiv preprint arXiv:2411.08508, 2024.
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in neural information processing systems, 33:7537-7547, 2020.
Yufei Wang. Zhihao Li, Lanqing Guo, Wenhan Yang, Alex Kot, and Bihan Wen. ContextGS: Com- pact 3d gaussian splatting with anchor level context model. Advances in neural information processing systems, 37:51532-51551, 2024.
Sebastian Weiss and Derek Bradley. Gaussian billboards: Expressive 2d gaussian splatting with textures. arXiv preprint arXiv:2412.12777 2024.
Rui Xu, Wenyue Chen, Jiepeng Wang, Yuan Liu, Peng Wang, Lin Gao, Shiqing Xin, Taku Komura, Xin Li, and Wenping Wang. SuperGaussians: Enhancing gaussian splatting using primitives with spatially varying colors. arXiv preprint arXiv:2411. 1.12411111966, 2024a.
Tian-Xing Xu, Wenbo Hu, Yu-Kun Lai, Ying Shan, and Song-Hai Zhang. Texture-GS: Disentan- gling the geometry and texture for 3d gaussian splatting editing. In European Conference on Computer Vision, pp. 37-53. Springer, 2024b.
Zongxin Ye, Wenyu Li, Sidun Liu, Peng Qiao, and Yong Dou. AbsGS: Recovering fine details in 3d gaussian splatting. In Proceedings of the 32nd ACM International Conference on Multimedia, pp. 1053-1061, 2024.
Zhaoliang Zhang, Tianchen Song, Yongjae Lee, Li Yang, Cheng Peng, Rama Chellappa, and Deliang Fan. LP-3DGS: Learning prune 3d gaussian splatting, Advances in Neural Information Pro- cessing Systems, 37: 11222244-1222555 2024.
A APPENDIX
A.1 ADDITIONAL EXPERIMENTS COMPARING OUR METHOD WITH BASELINES.
Table summarizes PSNR/SSIM/LPIP and memory for all methods under fixed Gaussian bud- gets (#GS IM, 500k, 100k, 50k, and 10k) on Mip-NeRF 360, Tanks& Tanks&Temples Temples, and DeepBlend- ing. Memory overheads are reported relative to the 2DGS baseline within each #GS block. Over- all, texture-based methods consistently improve over non-textured 2DGS variants in visual quality. Among them, Textured Gaussians* and BBSplat often achieve the strongest metrics, but require substantially more memory: Textured Gaussians* * typically around +1 10%% across budgets, while BBSplat becomes extremely heavy (about +1 +1760%) at #GS=100k. In contrast, ATG offers better memory-quality trade-off, matching or approaching the best-performing textured baselines with far lower overhead. This advantage is most pronounced at higher Gaussian counts (e.g.. 1M and 500k), where allocating high-resolution textures uniformly is most expensive and adaptive anisotropic tex- tures provide the greatest benefit. At very low #GS (50k/10k), the relative memory gap narrows because the baseline footprint is already small; however, these regimes are less critical in practice since high-fidelity reconstructions typically rely larger Gaussian budgets.
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Mip-NeRF
Temples
DeepBlending
PSNR SSIM
1 LPIPS
Mem (MR, +5)
PSNR
SSIM
I.PIPS
Mem (MB, +5)
PSNR
SSIM
T.PIPS 1 Mom (MR, +5)
#GS IM
2DGG
29.48
0.900
0.267
232 (0%)
20GS
29.58
0.899
0.266
232 (09))
2DGS5-MMME
28.37
0.838
0.172
232
23.11
0.827
0.148
232
0.898
232 (016)
Super
28.39
0.188
(28%)
0.847
284
0.900
(27%))
Textured Comssians
28:81
0.846
0:159
488 (110%)
23.58
0.831
(1 (1108)
03897
0.188
488 (110%)
A (Ours)
26.70
0.844
0,163
291 (25%)
23.58
0.831
0.145
292 (26%)
29.82
0.900
0,180
277 (198)
#GS
2DGS
27.38
0.805
0.283
116 (0%)
22.91
0.819
0.234
116 (0%)
29.21
0.894
0.286
116 (0%)
2DGS
27.77
0.811
0.270
116 (0%)
23.08
0..24
0.220
116 (0%)
29.45
0.8994
0.277
116 (0%)
3DGM
27.95
10824
0.196
116 (098)
22.87
0.819
0. 16166
116 (05%)
29.45
13.896
0.200
116 (095)
Caussians
28.01
0.831
0.215
1:88 (28%)
23.31
0.:35
0.206
148 (28%)
29.19
0.903
0, 0.266
148 (289))
0.898
A (Ours)
28.31
0.832
0.187
148 (28%))
23.43
0.824
0.163
150 (299))
29.71
0.899
0.193
140 (215))
(0%)
0.875
(0%)
2DGS
19.17
0.234
0.324
(0%)
28.32
0.879
0.324
24 (0%)
2DGS MCMC
26.40
0.267
0.294
(466)))
22.00
0:276
0.245
24 (096)
28.60
0.883
0.253
24 (096)
Super Gaussians
23.12
0.237
0.366
30 (285))
20.11
0.737
0.327
30 (28%))
26.61
0.875
0.331
30 (2856)
BBSplat
27.33
0.828
0,216
433 (1764%))
23:40
0.882
0.161
133 (166%)
28.78
0.899
0.268
433 (17668))
Textired Gonesians"
27.04
0.785
0.268
(11058)
22.52
0.787
0.233
0.890
(110%))
APTO (Qurs)
26.62
0.775
0.783
30 (28%)
22.37
0.780
0.243
31 (325)
0.888
(24%)
#GS SOE
2DGIS-MCME
0.359
(09))
BRSylat Textured Gonesians"
26.85
0.798
216
0.803
216 (17655)
03899
(176458)
26.08
0.748
0.329
(11088)
21.78
0.256
0.285
(11088)
28.59
0.860
0.271
25 (11098)
TTG (Ours)
25.64
0.736
0.347
15 (2698)
21.62
0.747
0.297
16 (315))
28.26
0.876
0.783
(245))
AG3
20GSS MCCC
22.81
0.618
0.536
3 (09%)
19:39
0.632
0.473
(0%)
25.13
0.822
0.476
3 (09)
Textured Goussiass
23.01
0.638
0.501
(110%)
20.05
0.654
0:412
(110)))
25.97
0.835
0.400
5 (1100%)
A (Ours)
0.628
0.518
(19%)
19.29
0.645
0.456
(25%)
0.827
0.421
(18%)
Table 4: Full multi-dataset evaluation. Memory percentages are relative to 2DGS at the same #GS. The top three results are highlighted in red orange and yellow and the least parameter increases are in bold
A.2 ADDITIONAL DETAILS ON COMPUTATIONAL EFFICIENCY
3D Gaussian Splatting (Kerbl et al.] 2023) is known for its real-time rendering capability. Our method builds upon 2D Gaussian Splatting (Huang et al.) 2024) by augmenting each Gaussian with a non-uniform, variable-sized texture. During rendering. after computing the uw-coordinates of the intersection between pixel and Gaussian along the camera ray, fetch texture values using bilinear interpolation.
To efficiently support non-uniform texture resolutions ranging from 21,2,4,.... N2) X {1, 4...., 2 where N is the number of texture upscaling, we pack all GausSaas textures into single large texture-atlas arry located in GPU global memory. Each Gaussian stores ad- ditional metadata specifying its texture dimensions and its offset within the atlas. In our custom CUDA rasterization kernel, this structure results in up 16 global-memory load operations per tex- ture lookup, since bilinear interpolation may require up to four texel samples for each of the RGBA channels.
Although these texture fetches introduce additional overhead during both training and inference, the system still maintains real-time rendering speeds above 30 FPS. While we did not explicitly optimize the rasterizer for maximum throughput, further improvements are likely possible through vectorized texture loads or tile-level shared-mer -emooyy prefetching.
Table repporss the inference FPS. per-frame inference time, and total training time for 2DGS (Huang et al. 2024). Textured Gaussians* (Chao et al. 2025), and TTG on the DeepBlending dataset (Hed- man et al. 2018 Both TT and Textured Gaussians" incur additional computation compared to 2DGS due to texture sampling, which explains the decrease in inference FPS. Importantly, A2TG is consistently faster than Textured Gaussians* during both inference and training. The key reason is that T allocates texture parameters adaptively rather than assigning a fixed-size texture to ev- ery Gaussian. This reduces the total number of texel reads and gradient updates, resulting in lower memory traffic and fewer texture operations. Consequently, A TG achieves higher inference FPS and shorter training time than Textured Gaussians* under the same number of Gaussians.
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Method
2DGS
A
Textured Gaussians
Number of Gaussians
500k
500k
500k
Model size (MB)
116
140
244
Inference FPS
250
140
119
Inference time per frame (s)
0.004
0.007
0.008
Training time (min)
7.18
19.7
21.6
Table 5: Computational Efficiency. Comparison of the inference speed under the same number of Gaussians on the DeepBlending dataset. The render speed of A and Textured Gaussians slower than 2DGS because of the extra texture sampling during rendering, while TT has slightly faster render speed than Textured Gaussians due to fewer texture parameters.
A TG (Ours)
Textured Gaussians'
GS 5000
GSS 100K
G55 - 100K
GS 5002
0.90
01955
PRNB
20 50
0.900
1300
21.55
0191
400
0197
16
4
16
15
Tecture Recoliatinss
Texturn Resobstions
Teatur Rmolition:
Tecnne Reselmioos
0.25
Size MMD
400
DSNR
SSIV
200
0:33
100
Textue Resolutons
Testure Resolation
Teatue Resolaion
Resebuiana
Comparison of different texture resolutions with fixed Gaussian budgets Deep- Blending, Textured Gaussians' uses a fixed per-Gaussian texture size, while AT adaptively allo- cates textures up to maximum cap.
Figure 6:
A.3 ADDITIONAL. EXPERIMENTS ON TEXTURE MAP RESOLUTION
To further study how texture resolution affects quality and memory, we compare Textured Gaussians" (Chao al. 2025 with TT under multiple texture resolutions. For Textured Gaussians", we sweep fixed per- Gaussian texture sizes from 2x2 2 to 16.16. For A ATG,, we evaluate the corresponding maximum texture caps (Max 2x2 Max 16x 16), where textures are adaptively allocated up to the specified cap.
Table 6 and Figure 6 summarize results under fixed Gaussian budgets (100k and 500k Gaussians). Overall, ATG provides a strong memory-qualiy trade-off compared to Textured Gaussians": it matches or improves PSNR/SSIM and reduces LPIPS while using less memory at lower texture resolutions. At higher texture resolutions, Textured Gaussians" can achieve slightly better quality, but the memory cost grows rapidly and becomes prohibitively expensive. Moreover, increasing texture resolution yields diminishing returns in visual metrics, which attribute to gradient- based texture upscaling procedure repeatedly prioritizing the same subset of Gaussians. In practice, these trends suggest that maximum texture cap of 2x2 or 4x4 is most appropriate for TTG Overall, the results highlight the benefit of adaptive texture allocation: it concentrates resolution where it is most beneficial while avoiding the uniform memory growth incurred by fixed-resolution textures.
A.4 DECLARATION OF L.M USAGE
We used large language models (LLMs) solely for polishing the text in this paper. Specifically, LLMss assisted in refining grammar, improving readability, and adjusting tone for academic writing.
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Method
Tex.
PSNR 1
SSIM 1
LPIPS +
Mem (MB)
#GS 500k
Textured Gaussians"
2x2
29.66
0.897
0.195
148.00
Textured Gaussians"
4x4
29.71
0.898
0.193
244.00
Textured Gaussians"
8x8
29.71
0.899
0.186
628.00
Textured Gaussians
16x16
29.69
0.898
0.178
2164.00
TG
Max x
29.77
0.900
0.191
127.66
TG
Max 4x4
29.81
0,900
0.191
136.24
TG
Max 8x8
29.83
0.901
0.191
159.95
A
Max 16x 16
29.80
0.901
0.189
232.82
#GS = 100k
Textured Gaussians"
2x2
29.12
0.888
0.243
29.60
Textured Gaussians"
4x4
29.18
0.890
0.236
48.80
Textured Gaussians"
8x8
29.22
0.893
0.222
125.60
Textured Gaussians"
16x16
29.34
0.894
0.210
432.80
A T
Max 2x2
29.08
0.889
0.243
26.44
A
Max 4x4
29.04
0.888
0.243
28.68
TTG
Max 8x8
29.09
0.889
0.242
35.39
A
Max 16x16
29.14
0.890
0.240
57.77
Table 6: Comparison of different texture resolutions with fixed Gaussian budgets Deep- Blending. Textured Gaussians" uses a fixed per-Gaussian texture size, while A2TG adaptively allo- cates textures up to maximum cap.
All research ideas, methodology, analyses, results, and terminology definitions presented in this work are original contributions from the authors and were not generated by LLMs.
15


Reviews:
[{"review_id": "xbq8qqGkdQ", "reviewer": "Reviewer_GiPV", "summary": "The paper introduces an enhanced 2D Gaussian splatting technique that incorporates texture maps with adaptively controlled resolutions. The intersection point of a ray with the tangent plane of the 2D Gaussian splat is computed within the CUDA rendering pipeline. Using the tangent plane coordinates (uv), the corresponding texture color is retrieved from the texture map. The resolution of the texture maps is dynamically adjusted based on the accumulated gradients of the local uv coordinates. This approach supports 'anisotropic' resolutions, enabling different levels of detail along the u and v axes. Experimental results demonstrate that the proposed method achieves superior rendering quality compared to state-of-the-art Textured Gaussians, while also reducing storage requirements.", "questions": "My suggestions are as follows: (1) provide a detailed explanation of the texture color query process using UV coordinates; (2) include an evaluation of the rendering speed; and (3) revise the conclusion section. I believe addressing these points will significantly enhance the quality of the paper, and I would be inclined to raise the score. While I acknowledge the novelty and contribution of the paper, improving these aspects is essential to further strengthen its overall quality.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Vanilla 3DGS and 2DGS require a large number of primitives to capture the high-frequency appearance and geometry of 3D scenes. This limitation arises from their restricted texture representation, which uses only a single color from a fixed viewpoint. Textured Gaussians, such as GStex, HDGS, Billboard Splatting, and SuperGaussians, represent significant advancements in this domain, enhancing Gaussian splatting with textures for improved 3D reconstruction. However, these methods do not account for the adaptive control of GS textures, leading to inefficiencies, as different primitives require varying levels of texture detail. Building on this insight, this paper introduces adaptive textures for GS, enabling variable resolutions for each primitive and along different axes. This approach is both innovative and practical.\n\n- The results are impressive and highly satisfactory. As demonstrated in Tables 1 and 2 and Figure 2, A^2GS achieves superior performance with reduced storage requirements thanks to its adaptive control of GS texture resolutions. Additionally, Figure 3 highlights the method’s superiority in reconstructing fine details.", "weaknesses": "The first weakness lies in the methodology and is quite fundamental. The paper does not clarify how textures are fetched based on UV coordinates. Is it through bilinear interpolation or nearest neighbor? If bilinear interpolation is used, Equation 6 omits the terms \\(\\frac{\\partial c_l(x_j)}{\\partial \\mu_{i,x}}\\) and \\(\\frac{\\partial c_l(x_j)}{\\partial \\mu_{i,y}}\\), as \\(c_l(x_j)\\) is linearly related to the UV coordinates. On the other hand, if bilinear interpolation is not used, the optimization process and model capability are constrained. Since the positions of Gaussian splats (GS) are jointly optimized, if the gradients of textures are not related to the intersection coordinates, the texture map gradients cannot effectively guide GS movements for better fitting. Furthermore, using nearest neighbor (NN) queries results in discontinuous textures, which could reduce overall quality.\n\nThe second weakness concerns the experiments. The paper does not evaluate rendering speed, an essential aspect of the approach. Because GS textures have varying resolutions, the indexing of each GS texture is non-uniform. As a result, within each CUDA grid, it is impossible to define a fixed-length shared array for threads within the grid and move the data in parallel from the global to the shared. Consequently, textures must be fetched from the global CUDA memory, which is slower. Additionally, calculating the cumulative sum of each GS texture resolution to obtain the global base index for each GS texture adds computational overhead. These factors likely impact rendering speed and should be evaluated to demonstrate the tradeoff between performance and speed. While a reduction in speed is acceptable to an extent, maintaining real-time performance (>30 FPS) is crucial. If the speed drops below this threshold, the limitation should be explicitly acknowledged as a significant constraint.\n\nThe third weakness pertains to the conclusion section. A visible but minor limitation is that the method does not address reducing texture resolution dynamically. For instance, if a GS is optimized to have finer textures during an incorrect stage of the process but later becomes smaller or moves to textureless regions, the high-resolution texture may no longer be necessary. Addressing this issue could further improve efficiency. Additionally, while the proposed method makes substantial contributions to GS textures, it raises the possibility of combining adaptive textures with flexible 2D primitives, such as Deformable Radial Kernel Splatting. This combination could enable both flexible boundary shapes and sharpness while preserving richer texture representations. These two points should be considered as limitations and opportunities for future work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7irS47KDie", "reviewer": "Reviewer_QVPU", "summary": "This paper proposes Adaptive Anisotropic Textured Gaussians (A2TG) to enhance texture representations by extending fixed square textures to adaptively allocated anisotropic resolutions, reducing memory consumption while improving image quality.\nThe resolution and aspect ratio are controlled by an adaptive gradient-based strategy during optimization.", "questions": "1. There are some contradictory claims about image-quality improvements (lines 53, 55–56, 66–67, and the Experiments section). Considering the proposed method is a special case of the fixed square resolution (albeit with more parameters), why does it improve image quality?\n2. How is the number of 100 K Gaussians guaranteed, since SfM initialization may exceed 100 K? Do you use low-resolution images or other strategies?\n3. According to the logic from lines 239–244, does the upscaling double the resolution when r is within [1/K_A, K_A], regardless of s_x or s_y values? This seems to contradict intuition.\n4. Does the anisotropic texture introduce runtime overhead?\n\nTypos\n1. Use \"texture-based\" instead of \"textured-based\".\n2. Line 314: replace “varies” with “various.”\n3. Line 318: change “resulting” to “resulting in,” and add “a” before “fixed.”\n4. Lines 322–323: the sentences are fragmented and hard to follow—please clarify why A2TG is the best.\n5. Line 367, 368, then to than", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper clearly identifies the problem of memory redundancy in textured Gaussians.\n2. The proposed anisotropic textures demonstrate good results in terms of storage efficiency.\n3. Using gradients to guide texture scaling is clear and well-motivated.\n4. The paper is well-organized and readable.", "weaknesses": "1. Some experiments could be added for further validation:\n1.1. The gradient-based densification follows the spirit of the adaptive density control procedure in 3DGS. Experiments re-enabling similar densification procedures in 2DGS or baseline models are desired and could further demonstrate the effectiveness of the proposed method.\n1.2. Since the performance of Textured Gaussians also depends on the resolution of textures, how does the proposed method perform compared with Textured Gaussians using 2×2 resolutions? Would the proposed method still achieve consistent advantages?\n1.3. What about results with more Gaussians (e.g. 1M)?\n2. Some mathematical notations are unclear, making it difficult to evaluate the correctness of the equations.\n2.1. For example, the c_{\\cdot}(x_j) notations in Eq.6 and Eq.7 refer to different concepts but lack explanations. Could the authors update the equations consistently for easier evaluation? From the current version, I suspect Eq. 7 may correspond to dL/d\\alpha instead of dc/d\\alpha. Since the labels are used interchangeably, a consistent version with clear explanations would help the final evaluation.\nThe second bullet on line 242: should it be r < 1/k_A?\n2.2. Various typos and hard-to-understand sentences remain; see Questions below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3DATDS6Yf5", "reviewer": "Reviewer_Cw5H", "summary": "The paper proposes Adaptive Anisotropic Textured Gaussians (A2TG), which equips each Gaussian primitive with an anisotropic, gradient-adaptive texture, improving memory efficiency and visual fidelity over fixed-square textures in real-time 3D scene rendering.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 1, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper introduces Adaptive Anisotropic Textured Gaussians (A2TG), a novel extension of textured Gaussian splatting that assigns each Gaussian an anisotropic texture whose resolution and aspect ratio are adaptively determined.\n- The paper is well-structured, with a clear motivation for adaptive texture allocation, detailed explanations of the method, and thorough evaluation of results.", "weaknesses": "While the proposed A2TG effectively improves texture allocation and memory efficiency, the method relies on several heuristically chosen thresholds for gradient-driven selection and anisotropy-based upscaling, which may require dataset-specific tuning. Additionally, the iterative upscaling procedure introduces extra computational overhead, potentially limiting real-time performance for very large or highly detailed scenes. Finally, the approach does not explicitly handle occlusion interactions beyond local gradients, which could affect texture fidelity in heavily occluded regions.\n\nThe paper has some typos:\n- Line 261, \"LPIPs\"\n- Line 314, \"... across varies textured-based 2DGS....\"\n- Line 322, \"... latter having more nubmer\"\n- Line 364, \"the visaul quality\"\n- Line 368-369, \"Textured Guassians*\"\n- Line 428, \" (w/o Uscaling)\"\n- Line 435-436, \"methods typical introduce\"\n- Line 441, \"among the-state-of-the-art methods\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oQ5AFAZvp7", "reviewer": "Reviewer_Q3gj", "summary": "The paper targets the inefficiency of assigning uniformly sized, square per-primitive textures in Gaussian-splatting pipelines. The authors propose A2TG on top of 2DGS: (1) start from a trained 2DGS model and attach per-Gaussian RGBA texture patches; (2) tie texture upscaling and aspect ratio to positional-gradient signals; (3) implement a lightweight, discrete allocation over {1,2,4}×{1,2,4} with a thresholded trigger and a periodic upscaling schedule. Experiments under fixed-memory and fixed-#Gaussians protocols on Mip-NeRF360, Tanks&Temples, and DeepBlending show comparable or better rendering quality at lower memory.", "questions": "1. Could you specify the exact values of the thresholds (e.g., the anisotropy thresholds **kA, kS**)?\n2. What motivates restricting texture sizes to the discrete set **{1,2,4}×{1,2,4}**? Did you try larger grids or continuous allocation, and how do they affect quality vs. memory?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- This paper is well-written and easy to understand.\n- This paper introduces anisotropic textures and achieves a slight reduction in memory footprint without compromising rendering quality.\n- The authors tie texture resolution to the gradient, enabling detail-aware texture optimization, which makes sense.\n- The ablations are appreciated to show the contribution of each part.", "weaknesses": "- Missing references for some important related works:\n  - [MM 2024] AbsGS: Recovering Fine Details for 3D Gaussian Splatting, by Zongxin Ye et al. AbsGS, similar to the `absolute value of its positional gradient` described in L229–230, is the key mechanism by which GS-based methods eliminate floating floaters.\n  - HDGS: Textured 2D Gaussian Splatting for Enhanced Scene Rendering, by Yunzhou Song et al. Like the present work, HDGS adopts a Texture-GS framework built on 2DGS, and therefore needs detailed discussion and comparison in the **Related Work** section.\n- While I appreciate the core idea proposed by the authors, the ablation in Table 3 suggests that the upscaling strategy and anisotropic textures contribute only marginally to the rendering quality. Their benefits are reflected primarily in reduced memory overhead. The improvements reported in Table 1 likely stem more from the inherently efficient memory management of 2DGS and the densification mechanism of 3DGS-MCMC.\n- There is no visualization of the textures. I understand it may be difficult to visualize textures at different levels of detail, but I do not think rendering quality is the primary aspect to focus on when introducing texture maps. We should care more about the quality of the textures themselves.\n- Also, no comparison on FPS and training time.\n- Minor typo errors:\n  - L53: `As the results` -> `As a result`\n  - L81: `build upon 3DGS` -> `builds upon 3DGS`\n  - L184-185: `are now calculated` -> `is now calculated`\n  - L197: `the pixel and alpha value` -> `the pixel and alpha values`\n  - L261: `the memory size of each algorithms resulting parameters`", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)