Anti-Aliased 2D Gaussian Splatting
Authors: Mae Younes
Adnane Boukhayma
Venue: NeurIPS 2025 poster ()
Keywords: Anti-aliasing
2D Gaussian splatting
Gaussian Splatting
Scene Representation
Surface Reconstruction
Primary area: applications

Abstract: 2D Gaussian Splatting (2DGS) has recently emerged as a promising method for novel view synthesis and surface reconstruction, offering better view-consistency and geometric accuracy than volumetric 3DGS.
However, 2DGS suffers from severe aliasing artifacts when rendering at different sampling rates than those used during training, limiting its practical applications in scenarios requiring camera zoom or varying fields of view. We identify that these artifacts stem from two key limitations: the lack of frequency constraints in the representation and an ineffective screen-space clamping approach.
To address these issues, we present AA-2DGS, an anti-aliased formulation of 2D Gaussian Splatting that maintains its geometric benefits while significantly enhancing rendering quality across different scales. Our method introduces a world-space flat smoothing kernel that constrains the frequency content of 2D Gaussian primitives based on the maximal sampling frequency from training views, effectively eliminating high-frequency artifacts when zooming in. Additionally, we derive a novel object-space Mip filter by leveraging an affine approximation of the ray-splat intersection mapping, which allows us to efficiently apply proper anti-aliasing directly in the local space of each splat.
Code will be available at https://github.com/maeyounes/AA-2DGS.

Anti-Aliased 2D Gaussian Splatting
Adnane Boukhayma INRIA France, University Rennes, CNRS, IRISA
Mae Younes
2DGS
AA-2DGS (Ours)
Reference
8x Zoom Out
Resolution
Training
4x Zoom In
Figure 1: 2DGS and AA- 2DGS under change of image sampling rate. We trained the models on single-scale images and rendered images with different resolutions to simulate Zoom In/Out, While they achieve similar performance at training scale, strong artifacts appear in 2DGS when changing the sampling rate. Our method (AA-2DGS) shows significant improvement in comparison.
Abstract
2D Gaussian Splatting (2DGS) has recently emerged as a promising method for novel view synthesis and surface reconstruction, offering better view-consistency and geometric accuracy than volumetric 3DGS. However, 2DGS suffers from severe aliasing artifacts when rendering different sampling rates than those used during training, limiting its practical applications in scenarios requiring camera zoom varying fields of view. We identify that these artifacts stem from
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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key limitations: the lack of frequency constraints in the representation and an ineffective screen-space clamping approach. To address these issues, we present AA-2DGS, an anti-aliased formulation of 2D Gaussian Splatting that maintains its geometric benefits while significantly enhancing rendering quality across different scales. Our method introduces world-space flat smoothing kernel that constrains the frequency content of 2D Gaussian primitives based on the maximal sampling frequency from training views, effectively eliminating when zooming in. Additionally, we derive a novel object-space Mip filter by leveraging an affine approximation of the ray-splat intersection mapping, which allows us to efficiently apply proper anti- aliasing directly in the local space of each splat. Code will be available at at/AA- ADGSS
feeuefry artifacts
1 Introduction
3D reconstruction from multi-view images has been a fundamental problem in computer vision, graphics, and machine learning for decades. This field has seen renewed interest due to its applications in autonomous driving. medical imaging, gaming, visual effects, and extended reality experiences, which all require high-quality 3D modeling and visualization.
Neural Radiance Fields (NeRF) 43 revolutionized this area by introducing a neural representation that models scenes through differentiable volume rendering. Building on this foundation, 3D Gaussian Splatting (3DGS) 31 recently revitalized point-based graphics by replacing neural networks with explicit 3D Gaussian primitives. These primitives are rasterized and rendered via volume resampling and their parameters are optimized through gradient-basee inverse rendering. With its efficient density control, primitive sorting, and tile-based rasterization, 3DGS achieves state-of-the-an art novel view synthesis while enabling real-time rendering and requiring shorter training times.
The Gaussian Splatting approach has evolved into two primary variants: 3DGS [1] and 2D Gaussian Splatting (2DGS) [23]. While 3DGS represents scenes using volumetric 3D Gaussian primitives, 2DGS employs flattened 2D Gaussian disks embedded in 3D space. This distinction is significant: 3DGS projects 3D Gaussians onto the screen to obtain 2D screen-space Gaussians, which are then rendered. In contrast, 2DGS evaluates the Gaussians directly at ray-splat intersections in the local coordinates of each planar primitive. This approach gives 2DGS superior geometric accuracy, particularly for depth and normal reconstruction, making it valuable for applications requiring precise geometry such as mesh recovery [10, physics-based rendering 18] and reflectance modeling (69]
Despite its strengths, 2DGS faces a significant challenge: its formulation complicates the integration of proper anti-aliasing techniques. The 2DGS method attempts to address this by employing screen-space lower bounding approach (clamping) [5], but our investigation reveals that this approach often exacerbates aliasing artifacts rather than mitigating them. This is particularly evident when rendering at different sampling rates, such as zooming in or out from a scene (See Tab. Tab. 2
and 3
Recent work on Mip-Splatting 75 has identified two key sources of aliasing in 3DGS: the lack of 3D frequency constraints and inadequate screen-space filtering. Mip-Splatting addresses these issues by introducing a 3D smoothing filter to constrain the frequency content of primitives based on training view sampling rates, and by replacing the traditional screen-space dilation with Mip filter that better approximates the physical imaging process. However, these solutions cannot be directly applied to 2DGS due to its fundamentally different primitive representation and rendering approach.
In this paper, we present Anti-Aliased 2D Gaussian Splatting (AA-2DGS), an approach that makes two key contributions: We introduce a world-space flat smoothing kernel that constrains the frequency content of 2D Gaussian primitives based on the sampling rates of the training views. This addresses fiequueyy artifacts when zooming in scene by ensuring that the primitives respect the Myquist-Shannon sampling theorem (55). We derive an object-space Mip filter that leverages affine approximation of the ray-splat intersection mapping used in 2DGS. This allows us to incorporate Mip filtering directly in the local space of each splat, where the Gaussian evaluation occurs. The resulting formulation is both mathematically elegant and computationally efficient. Ablative analysis of these two components is in the supplementary material.
We evaluate AA-2DGS on standard novel view synthesis datasets, including Mip-NeRF 360 (2 and Blender 4 J as well as the DTU (29 mesh reconstruction benchmark. Our results demonstrate that AA- 2DGS consistently outperforms the original 2DGS method, particularly under challenging

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conditions such as varying sampling rates and mixed resolution training. Importantly, our approach maintains the geometric accuracy that makes 2DGS valuable while significantly reducing aliasing artifacts.
2 Related Work
Until recently, implicit representations cou- pled with differentiable volume rendering have been at the forefront of 31) shape and appearance modeling. NeRFs [43] model scenes with an implicit density and view de- pendent radiance, parametrized with MLPs. Anti-aliase Anti-aliasing can be implemented in these representations through cone tracing and pre- pre-fitering the input positional or feature encodings [
World
Space Filter
Si Sirvan (M)
mJ
Screen Space Filter
CDD
(m)
Figure 2: Overview. We constrain the maximum fre- quency of our 21) Gaussians (Red) to limit estimated from the training images with world-space flat smooth- ing filter. Next, leveraging an affine approximation of the mapping from screen space to local splat space: my where Ou 35 we can express the reconstruction ker- nel footprint in screen space (Blue). This enables the integration of a screen space anti-aliasing Gaussian fil- ter (Green). Via the affine mapping, we can revert to final simpler and computationally lighter expression of our kernel (Orange) defined in local splat space.
22 [1]. Multiscale volume rendering requires intensive MI.P querying, thus limiting the rendering frame rate. This issue can be alleviated with grid based representations [44 58 14
15 8).
These can struggle with large unbounded reconstruction, despite Level-of-detail Oc-
trees [40]. By expressing density as a func- tion of signed distance field, NeRFs lead to powerful geometry recovery methods
62 70
38
73
26
66
Implicit recon-
struction has been robustified against noise and sparsity from both image and point cloud input using and various regularizations (e.g.
generalizable data priors (e.g. 77
30
36
46
24
48
49
52
[45 68 25 12
37
51
47
50
49
3D Gaussian splatting 31 subverted this trend lately. Combining volume rendering 42 and EWA splatting
8 within an efficient inverse rendering optimization [33]. has spawned substantial research due to its remarkable novel view performance and high rendering frame rate. Extensions include generalizable models 77 [1 (8 660, bundle adjustment [80
[16] (27 [1], higher dimensional spatiotemporal models [65] in
primitives [3), more expressive texture splatting [54. 59
6
addition to several methods to improve density control 32
61 78
model compactness [35
6. and
training speed 34 79 20) Recent work augmented 3DGS's anti-aliasing abilities. 39 analytically
approximates the integral of Gaussian signals over pixel areas using a conditioned logistic function. However, calculating integrals for every pixel can be computationally intensive, especially for high- resolution images and large-scale scenes. (67 represents the scene with Gaussians at multiple scales, rendering higher-resolution images with smaller Gaussians and lower-resolution images with fewer larger ones. This strategy can lead to important memory overheads nonetheless. (57) uses frustum- based supersampling strategy to mitigate aliasing, which can be computationally costly. especially at higher resolutions. Closest to our contribution, [75) reinstated the EWA screen space filter in 3DGS, and proposed to use a 3D low-pass filter band-limit the 3D Gaussian representation based on the sampling limits of the input images. The 2DGS (2 representation uses planar primitives instead of volumetric ones. It offers competitive novel view synthesis and state-of-the-ar mesh reconstruction performance, where 3DGS fails to faithfully recover depth. To the best of our knowledge, ours is the first work that analyses the anti-aliasing capabilities of 2DGS, and proposes solution to its limitations in this department.
3 Method
Our method extends the 2DGS framework by incorporating sequencyy-bseed filtering techniques to address aliasing artifacts across varying sampling rates. We first review the sampling theorem and 2D Gaussian Splatting to establish the foundation for our antialiasing techniques. Then, we introduce our key contributions:
(1) world-space flat smoothing kernel that effectively limits the frequency of the 2D Gaussian primitives based on the sampling rate of training views, and (2) an object-space Mip filter that leverages the ray-splat intersection mapping to accurately perform antialiasing directly in the local space of each splat.
3

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3.1 Preliminaries
Sampling Theorem The Nyquist- Shannnn Sampling Theorem [35] states that band-limited signal with no frequency components above can be perfectly reconstructed from samples taken a rate 2 2v. Otherwise, aliasing occurs as high frequencies are incorrectly mapped to lower ones. To prevent this, low-pass filter is applied prior to sampling to suppress frequencies above E.2 This principle guides our antialiasing strategy in 2D Gaussian Splatting.
represents scenes with oriented planar Gaussian disks in 3D space. Each 2DGS primitive has center Pk R3, two orthogonal tangential vectors ta and t, and scaling factors s = (Su, sy). Using rotation matrix R. [ta; tu tu ty] and scaling matrix the geometry of the primitive is defined in the local tangent plane parameterized by:
2D Gaussian Splatting 2DGS [3
P(u, v) = P& + Sutyl + Spt,p (u, v, 1, 1,),,
(1)
where
H
Su tu 0
8p
p 1
RS
(2)
0
1
For point u (u, e) in the primitive local space, the kernel value writes:
(- u² + 2²
97 (u) cxp exp
(3)
where I is the identity matrix, representing the covariance of the Gaussian in its local coordinate system. Rendering preformed via volumetric alpha blending using primitive opacity a and color C₁:
C(x)= Ec, a. (((()))
II aj (u(xx)))
(4)
Ray-Splat Intersection 2DGS employs a ray-splat intersection method based on [56 64 for rendering. Given an image coordinate x = (a, y), the splat intersection the intersection of xr-llane, y-plane, and the splat plane. Homogeneous representations of the x-plane and y-plane are h (-1,0, O, 0, x) and h, (0, 1, y) These planes are transformed into the local coordinate system of the splat using:
h (WH) hyy h (WH) hy
where W is the world to screen space transform matrix. The intersection point in local coordinates u(x) then writes:
h h h h h h
h h h h h2
x(x)
e(x)
(6)
where h2 and h denote the i-th component of the 4D planes.
Antialiasing Challenges in 2DGS The original 2DGS implementation addresses the issue of degenerate cases (when a Gaussian is viewed from slanted angle) by employing an object-space low-pass filter:
((x) max { oP 9
where c the projection of the center Pş: and a scaling factor. This mechanism was inspired by the heuristic EWA approximation in (5) that was proposed to handle minification and aliasing when EWA filtering not possible.
While clamping improves rendering stability, it has notable drawbacks. First, the use of max operation introduces discontinuities in the gradient flow, potentially hindering optimization. Second, the conditional logic in Eq. 7 can cause thread divergence in CUDA warps, reducing GPU efficiency, Third, the heuristic compares distances different domains (local splat space vs. screen space) and lacks the antialiasing quality of true screen space EWA filtering. Even standard EWA can suffer from aliasing and over blurriness (as demonstrated by Mip-Splatiing (75)), issues worsened by this approximation. In the following, we present our solution to these challenges.

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3.2
World-Space Flat Smoothing Kernel
The goal here is to constrain the maximum frequency of the 3D representation during optimization based the Nyquist limit of training views, highlighted by [75]. However, unlike 3DGS, our primitives are planar. Therefore, we need to adapt the 3D smoothing filter concept to our flattened primitive representation.
Multiview Frequency Bounds Following the analysis in 77 determine the maximal sampling rate for each primitive based on the training views. For an image with focal length f in pixel units, the world-space sampling interval π' at depth is 7 = 1/2 d where ú is the sampling frequency. We determine the maximal sampling rate for primitive A as:
{ (px m=l...N
max
(8)
dn
where is the total number of training images, and (p) is an indicator function that evaluates to true if the Gaussian center Pl: falls within the view frustum of the n-th camera.
Flat Smoothing The 3D smoothing filter in [75] convolves each 3D primitive Gaussian Gz, with an isotropic 3D low-pass filter 9jo Gr Ts with o smonthh fing Sreg being hyperparameter.
This results in a 3D Gaussian with covariance En smmrrth 13.
Our 2D Gaussian primitives are embedded on 2D planes. In the splat plane coordinate system (spanned by t. tv, centered at Pt).. this primitive intrinsically represents 2D Gaussian distribution with covariance VA diag(s? 2 ). To achieve similar band-limiting effect while keeping the primitive flat, we project the isotropie 3D smoothing kernel Goo onto the plane of the 2D Gaussian primitive. This projection yields an isotropic 2D Gaussian filter with the same variance 02 smooth, T2 in the planar coordinates defined by (twg L. Next we convolve the primitive's 's intrinsic 2D Gaussian (covariance V&)) with this projected 2D smoothing filter (covariance o2 & I2), both on the splat's smooth,
plane. This convolution yields a new 2D Gaussian on the same plane with covariance:
VefT
2 smooth,
($22
2 smooth,
0
o smooth,
To maintain energy conservation, the primitive's opacity on is modulated, analogously to 75 For unnormalized Gaussians, this factor is the ratio of the product of scales:
Of
smooth
CAk
Sup. 80k
(10)
+ 0 smooth,
Pug
smooth,
The maximal sampling rates Dz. and thus 2 smooth, k are computed based on the training views and remain fixed during testing. This world-space flat smoothing effectively regularizes the 2D primitives by ensuring their footprint on their respective planes adheres to the sampling limits, preventing high-frequency artifacts when zooming in, analogous to its 3D counterpart.
3.3 Object-Space Mip Filter
While the flat smoothing kernel addresses pre-aliasing from the representation itself, we also need to handle aliasing during rendering, especially when projecting splats to screen resolutions that differ from training (e.g., zooming out). Standard 3DGS and Mip-Splatting apply screen space filters. However, 2DGS evaluates Gaussians ray-splat intersection points un (x) in the splat's local space, making direct application screen space filter non-trivial.
Ray-Splat Intersection Affine Mapping The key insight of our approach is to leverage the ray- splat intersection mapping used in 2DGS and derive an affine approximation of it, adapting the principles of Elliptical Weighted Average (EWA) filtering 21 82 (53] to the 2DGS framework. This allows to map screen space Mip filter to the local space of each splat, where the Gaussian evaluation actually happens.
Let m be the mapping from pixel coordinates x to local splat coordinates u. Let us approximate this mapping using a first-order Taylor expansion around pixel location Xo:
mxx) m(xo) + J (x xx) us + J(x xo),
(11)
5

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where Uo (xx) is the intersection of the ray passing through Xy with the splat, and J is the Jacobian of the mapping evaluated at Xp- captures how the local coordinates change with respect to small changes in pixel coordinates, and can be computed analytically from the derivation of the ray-splat intersection formula (Fq 60:
du
au
J
Oy
(12)
Oy
0v
dy
Mip Filter Mapping Using properties of Gaüssian functions under affine transformations, express the 2D Gaussian in screen space as:
o (u) = (m(()) = J -
(13)
To perform antialiasing, convolve this transformed Gaussian with Mip filter in screen space. Similar to Mip-Splatting, we use a Gaussian Mip filter with covariance aI to approximate the box filter of the physical imaging process, but note that we can also use the EWA filter here:
Gupp (x)
( 93 P (x)
(14)
Using the property that the convolution of two Gaussians results in another Gaussian with the sum of their covariance matrices, we get:
| -
mip (x)
(15)
[J]
Mapping Back to Object Space While we could evaluate the Mip filtered Gaussian directly in screen space, it more efficient to map it back to the local space of the splat. Using the properties of Gaussian functions under afline transformations again, we get:
Geip (x) = - (u).
(16)
We denote the new covariance in local space: E (x) = I + GJJ. The mip filtered Gaussian contribution for splat l at pixel x is then evaluated local uv-space at (x):::
1 (-2 (x() (2ioaat, (x)) (kx)))-ux((3 (x)(xx)
Gobj-mip.A (x)
V E [xcaa,
exp
(17)
Our object-space Mip filter eliminates the computational overhead and numerical instabilities of screen space evaluation. Unlike object-space EWA splatting (53] which approximates perspective around the primitive center, we center the affine approximation per pixel for improved accuracy, especially with large primitives or challenging views.
4 Experiments
We evaluate work through novel view synthesis datasets Blender 43) and Mip-NeRF 360 2 following the benchmark in Mip-Splatting [75). These experiments assess generalization to both in and out of distribution pixel sampling rate. We additionally evaluate our work through the 3D surface reconstruction experiment on dataset DTU [29] following the benchmark in [23]. We provide additional results, ablations and extended discussion limitations in the supplementary material.
4.1 Implementation Details
We build our method upon the open-source implementation of 2DGS. Following Mip-Splatting, we train our models for 30K iterations across all scenes and use the same loss function, Gaussian density control strategy, schedule, and hyperparameters. For novel view synthesis experiments, we disable the depth and normal regularizations used by 2DGS and enable them for surface reconstruction experiment. We follow the Mip-Splatting approach and recompute the sampling rate of each 2D Gaussian primitive every m 100 iterations. Similarly, we choose the variance of our object-space Mip filter as 0.1, approximating a single pixel, and the variance of the flat smoothing filter as 0.2. We implement our object-space Mip filtering with custom CUDA kernels for both forward and backward computation. Due to the extra computations required by the Mip filter, our approach incurs an overhead of 15-30% in rendering time compared to the aliased 2DGS. We conduct all experiments on NVIDIA RTX A6000 GPUs.

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4.2 Evaluation on the Blender Dataset
The Blender dataset 43 includes 8 synthetically rendered scenes with complex geometry and realistic materials. Each scene has 100 training views and 200 test views, rendered at 800x800 resolution.
Multi-scale Training and Multi-scale Testing We train our model with multi-scale data and
evaluate with multi-scale data following previous work [ 22 75]. We adopt the biased sampling strategy in [75 [2] where rays from full-resolution images are sampled at a higher frequency (40%) compared to those from lower resolutions (20% per remaining resolution level). This ensures greater emphasis on high-resolution data while maintaining coverage across all image scales. Table shows the quantitative results of this experiment. Except for 2DGS variants, we report numbers for other methods from [75] We outperform the 3DGS based Mip-Splatting and state-of-the-a Nerf based methods MipNeRF and Tri-MipRF on average PSNR, and also almost across most scales. Notice that we outperform the 2DGS baselines with large margin across all scales, This shows that our Object-Space Mip filter enables the model to handle varying levels of detail without overfitting on a single scale. On the other hand, we showcase the finding that the screen space clamping heuristic hinders the performance of vanilla 2DGS at the higher scales.
PSNR1
SSIM
LPIPS
Pall 1/s Res.
1/4
Res.
Avg.
Res.
Res.
1/4 Ros.
Res.
Avg.
Res.
Res.
1/4
Avg.
NoRPP wilPPw/o.o.. Cami
13
31,20
30.65
25.25
22.53
27.66
0.950
0.956
0,930
0.871
0.927
0.055
0,034
0.043
0,075
0.082
NaRF (45)
29.90
92.13
$3.40
29.47
$1.23
0.938
0.959
0.975
0.962
0.958
0.044
0.040
0.024
0.899
0.044
MipNeRF
32.63
34.34
35:47
35.60
34.51
0.958
0.970
0.979
0.963
0.973
0047
0,026
0.017
0.012
0.06
Plennees
31.60
32.85
51.35
25.63
30.34
0,335
0367
0.961
0.9%
0.935
0031
0.032
0.445
0077
0/01
TemsokF
32.11
33.03
30.45
26.80
30.60
0,956
0.966
0.962
0.939
0.956
0.06
0.038
0.047
0.076
0.054
ImstantNGPP
30.00
32.15
33.31
31.20
0.026
0.400
Tri-MigHt
32.65
34.24
35.02
35.53
54.36
0.958
0971
0.980
0.447
0.027
0.18
0.012 0.026
3DGS
28.79
30.66
31
27.08
79.77
0.943
0.962
0.972
0.960
0.960
0.065
0.058
0.055
0,31
0.040
TXXS 41
FMA [2]
$1.34
31.25
33.78
31.48
3500
0.961
0.933
0.979
0:963
0.974
0.041
1.00%
0101
0111
0007
Mip-Splaiie Saap-SSlliing [55]
32.81
34.49
35.45
35.50
34.56
0.967
0.977
0.983
0,963
0.979
0.035
0.019
0.013
0.010
0.019
TDXSS 223
28.38
30.24
31.42
27.35
25:40
0,335
0335
0.970
0.93
0.934
0078
0,047
0009
0192
01547
2DGS Clarsinng
31.64
33.33
31.61
27.62
31.08
0,960
0.975
0,957
0,966
0.045
0,023
0.028
0,056 0.008
AA-2IXGS
$2.68
0.965
0.10
Table 1: Multi-scale Training and Multi-scale Testing on the Blender dataset [43]. Our approach signifi-
cantly improves 2DGS and achieves comparable or better performance than Mip-Splatting
Single-scale Training and Multi-scale Testing Following (75,, we train on full resolution images and test at various lower resolutions (1 1/2. 1/4, 1/8) to mimic zoom-out effects. Table [Ishows the quantitative results of this experiment. The Clamping deteriorates the performance of 2DGS in this experiment as well. Our method outperforms all anti-aliased 3DGS and NeRF based competition in average PSNR and also across all resolutions, with a large improvement with respect to the baseline 2DGS. This is testimony of the effectiveness of our Object-Space Mip filter combined with the accurate ray splat intersection rendering. These results are clearly reflected in the qualitative superiority of our renderings especially at lower resolutions compared to training, as shown in Figure 3 or also the zooming out visualization in the teaser Figure I
PSNK f
SSIM f
LPIPS
Full Res.
1/6 Res.
3/5 Res.
1// Res.
Ave
Full Res.
1/6 Res. 1/4 Ros.
1/4 Ros.
Aug.
Full Res.
1/s
Ros.
Res.
U/s Res
Ave
NORF 44
31.43
32.43
30.25
26.70
30.25
(1349)
13362
01394
0031
03356
01241
0.044
1.307
MinNoRF [
33.08
33.31
30.91
27.97
31.31
0.961
0.970
0.969
0.961
0.965
0.045
0.031
0.636
0.852
0.041
13
32.31
K3.1
25.45
Instan: NCP
33.09
33.00
26.33
0962
0.969
0.917
0.044
0.046
0.019
Tri-MieRE
32,89
32.84
25.87
29.47
0.588
0.967
0913
0.47
0.946
0.033
0.046
03075
0.050
3DGS
33.33
26.95
21.38
17.69
24.84
0.969
0.949
0.875
0.766
0.800
0.030
0.022
0.066
0.121
0.063
MICI
11
+ FWA 83
11.51
$1.66
27.82
24.63
20.20
(1009)
0.971
0.09)
0040
0360
0.032
0024
0.035
0.047
03032
Mip Splating (5
33.36
34.00
31.85
28.67
31.97
0.969
0.977
0.978
0.933
0.71
0.031
0.019
0.019
0.026
0.024
21008
[A]
33,05
27.64
20.61
16.59
24,47
0367
0.952
0.856
0.720
0374
0.033
01037
0,022
0.151
0.016
2DGS wio Clamping
33.18
33.04
26.21
30.56
0.968
0.973
0.964
0.45
0.032
0.24
0.040
0.054 0.042
AA-IDGS
33.24
0.967
0.976
0.034
0.024
Table 2: Single-scale Training and Multi-scale Testing on the Blender Dataset (43] All methods are
trained on full-resolution images and evaluated at four different (smaller) resolutions, with lower resolutions simulating zoom-out effects. AA-2DGS yields comparable results at training resolution to 3DGS- methods and achieves significant improvements compared to other methods in almost all metries at different lower scales.
4.3 Evaluation on the Mip-NeRF 360 Dataset
The Mip-NeRF 360 Dataset 2 is designed to evaluate rendering methods in unbounded, real-world 360 scenes with complex backgrounds, varying lighting, and challenging view-dependent effects. It consists of 9 real-world indoor and outdoor scenes. Each scene contains 100 to 400 training images and 200 test images.
7

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Chair
Lego
Mip- Splat.
2DGS
2DGS
AA-2DGS
Mip- Spla-
2DGS
2DGS
AA AA-2DGS
Full
1/4
1/8
Ficus
Ship
Mip- Splai.
2DGS
2DGS wko Cl.
AA- M--S
GT
Mip- Splai.
2DGS
2DGS vno CL.
AA-2DGS
GT
Fail
Figure 3: Single-scale Training and Multi-scale Testing on the Blender Dataset [43]. All methods are trained at full resolution and evaluated at different (smaller) resolutions to mimic zoom-out. Our method (AA-2DGS) consistently demonstrates improved quality across all sampling rates compared to the baseline 2DGS method.
Single-scale Training and Multi-scale Testing Following 75 we train here 1/8 resolution images and test at various higher resolutions (1 x, 2x, 4x, 8x) to simulate zoom-in effects. Results are shown in Table We perform on par here with the state-of-the-art anti-aliased 3DGS, with considerable improvement as compared to the NeRF based methods due to their MLP overfitting. Removing the clamping from 2DGS increases its performance. Our healthy margins with respect to the 2DGS baselines demonstrate the utility of our flat smoothing kernel for frequency regularization. This can be visualized in the qualitative comparison of Figure 4 where AA-2DGS shows reduced aliasing artifacts compared to its baselines and renders fine details with more fidelity without aliasing, We also find that the clamping heuristic hurts the performance of vanilla 2DGS. We note that while the flat smoothing kernel improves results for this magnification experiment, the nature of 2D planar primitives makes them more likely to become extremely thin during training at low resolution. When rendered at higher resolutions, they appear as hieele-llike" artifacts because they're 're too small/thin relative to the display resolution.
PSNR
SSIM
LPIPS
1x Res. 2x
Res.
4x
Res
8x
Res.
Agg.
1x Res.
2x
Res
4x Ros
S Res
A0g
1x Res.
2x
Res.
Res.
S Res. Jug.
Iestarr SGS [41)
16.79
31.16
21.11
21.11
25.0
17230
11A8
127
0.29
0.30
0.133
U.313
Mip- NcRF 340 2
29.26
25.18
24.16
24.00
25.67
0.560
0.727
0.670
0.706
0.74
0.12
0.360
0.370
0.428
0.205
2900
23.27
20.87
20:21
31.32
0.75
0/600
0.366
0159
0870
8087
0.577
0.411
0.491
0.318
26.19
23.50
20:71
0.619
0.619
0.715
0.476
29.30
25.90
23.30
22.31
0.755
0,677
0.45
0.44
0.305
0,449
Syl-Smin
0.51
2003 Clampong
28.09
26.64
25/64
26,66
0.555
0.14
0.729
0.667
0.241
0.347
0.411
AA-2DGS (ound)
29.30
23.16
26.10
25.77
23.08
0.877
0.795
0.732
0.35
0.765
0.101
0.155
0.329
0411
0.66
Table Single-scale Training and Multi-scale Testing on the Mip-NeRF 360 Dataset (2). All methods
are trained on the smallest scale (1x) and evaluated across four scales (1x, 2x, 4x, and 8x),
with evaluations at higher sampling rates simulating zoom-in effects. Ours method significantly improves on the baseline 2DGS method across all scales even on the training resolution while having competitive results to Mip-Splatting
Single-scale Training and Same-scale Testing We perform here the standard benchmark evaluation on the Mip-NeRF 360 dataset (2, where models are trained and tested at the same resolution. Indoor scenes are downsampled by a factor of 2 and outdoor by 4. Table 5 shows that 3DGS based methods perform slightly better than the 2DGS based counterparts in this setting, where our method still comparable to the baseline 2DGS method. Note that antialiasing methods like ours involve an inherent
8

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Mip-Splatting
2DGS
2DGS w/o Clamp.
AA-2DGS (Ours)
GT
tx
X
Figure Single-scale Training and Multi-scale Testing on Mip-NeRF 360 dataset (2 All models are trained on 1/8 resolution and tested at different upscaling factors. Our AA-2DGS method maintains high fidelity when rendering at resolutions higher than the training resolution, reducing magnification artifacts compared to the baseline 2DGS method.
trade-off: by hand-limiting the representation to prevent aliasing artifacts, we necessarily attenuate some high-free figh-frryyency content. This manifest as small decrease in peak sharpness at the original training resolution, resulting in slightly lower PSNR compared to the non-antialiased baseline method which is fundamental trade-off between aliasing and sharpness. The minor reduction in single-scale PSNR vastly outweighed by the significant improvements non-training scale rendering, demonstrated in the previous experiments.
NeRE
1:3c
1.50
1:35
1533
35
2.25
1.27
43
1:41
157
S1
218
ST
137
IIS
033
433
11
130
-135
115
0000
131
1.07
NJF P3
23.55
34.04
SIMM
0/05
LPES
0.51
Natak [T0
Noss lesdd 53
Neis (2)
0.20
0.6
0.43
0.72
0.5
0.31
0.35
225
1335
0.53
2.35
1.36
1:34
0.54
1.31
0.33
035
2.0
1.29
1.15
130
0.90
0.78
0.5
0.9
L.35
1047
032
1000
0.64
3.5
131
1.0
10:55
0.52
0.15
0/66
0.41
0.00
1-22
6.53
0443
0.8
0.8
Docart NCT
Zip NeBE
Picenach 13
23.08
25.66
47.23
9.58
8X36
0,11
6.705
8308
0443
0100
Table 4: Quantitative comparison the DTU Dataset [29). We use reported Table 5: Single-scale Training and
2008
M--D0S5
Chapping
1030
0077
0.55
1830
0111
0.5
1111
031
111h
1756
0.56
18
1.56
0.30
6.26
033
0:00
'576
010
2008
AA-DXS
wirckeyjng
19
27.38
22.39
16
0.316
0.319
0302
0226
Chamfer distance results from [76]. indicates that we retrain the model.
Testing on Mip-NeRF 360 dataset (2) In the standard in- distribution setting. our approach still demonstrales performance par with the baseline 2DGS method.
4.4 Evaluation on the DTU Dataset
The DTU dataset 29 counts 15 scenes, each with 49 or 69 images. We use downsampled images to 800x600. We follow previous methods 76 23 for this evaluation. We report reconstruction performances in Table 4 NeuralAngelo 33 is among the state-of-the-ar methods in this benchmark. However, Such implicit methods can be very slow train, taking more than hours at times on standard GPUs. The 3DGS representation evidently fails to recover meaningful depth despite good

---

novel view synthesis performance. AA-2DGS, 2DGS and 2DGS w/o Clamping perform almost similarly, with a slight edge in favor of our method. This shows that our anti-aliasing mechanisms integration within the 2DGS representation preserves its geometric modelling capabilities. The performance we obtain is on par with recent stat-of-the-art Gaussian Splatring based reconstruction methods. Additionally, we note that the benefits of our anti-aliasing method are not confined to RGB output, but naturally extend to all rendered attributes as shown through normal rendering in figure S This can improve accuracy in applications like surface reconstruction and reflective scene modelling, especially when multiscale input images are used for the training.
2DGS
Ours
2DGS
Ours
GT
2x Zoom In
4x Zoom Out
E TE
Normal Rendering
RGB Rendering
Figure 5: 2DGS and our method's RGB and normal rendering under different image sampling rates than the training views. We show results of simulating Zoom In (2x) and Zoom Out (4x). In addition to anti- anti-aliased color rendering, our method also improves other attributes rendering.
Limitations
While our method significantly reduces aliasing in 2D Gaussian Splatting, is not without limitations. A fundamental issue stems from the planar nature of 2D Gaussians, which can still produce "needle- like" artifacts magnification scenarios or in extreme grazing angles viewing. Our world-space smoothing mitigates this by enforcing a minimum screen footprint, but it cannot fundamentally solve the zero-thickness problem in the direction normal the primitive.
Furthermore, our approach involves a classic trade-off between antialiasing and detail preservation. The fixed filter parameters, while effective in general, may not be optimal for all scenes and can lead to over-smoothing A more detailed analysis of these limitations, is provided in Appendix C
6 Conclusion
We introduced Anti-Aliased 2D Gaussian Splatting (AA-2DGS), a method that enables high-quality antialiasing for 2D Gaussian primitives while preserving their geometric accuracy. Our approach combines world-space flat smoothing kernel that constrains the frequency content of 2D Gaussian primitives based on training view sampling rates, and an object-space Mip filter that leverages the ray-splat intersection mapping perform prefiltering directly in the local space of each splat. By incorporating these techniques, AA-2DGS effectively mitigates aliasing artifacts when rendering at different sampling rates. Our experiments demonstrate that AA-2DGS consistently outperforms the original 2DGS method across standard novel view synthesis benchmarks for varied sampling rates and mixed resolution training while maintaining mesh reconstruction capabilities. This work bridges the gap between the geometric accuracy of 2DGS and the high-quality antialiasing capabilities previously only available to volumetric 3D Gaussian representations, enabling more robust and visually pleasing results in applications requiring precise geometry.
We not identify any specific societal risks that require special attention within the scope of this work.
Potential Societal Impact
10

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Acknowledgment This work was granted access to the HPC resources of IDRIS under the allocation 20XX-AD010616156 made by GENCI.
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Appendix: Supplementary Material
In this Appendix, we first present ablation studies of our method AA-2DGS in A We provide additional qualitative results in B We then provide further discussion on the limitations of our method in C
A
Ablation Studies
A.1
Effectiveness of the World-Space Flat Smoothing Kernel
PSNR 1
SSIM 7
LPIPS 1
1x Res.
2y Ros
4x Ros.
8y
Res.
Avg.
1x Ros.
2x
Res.
4x Ros. 8x Ros
Avg.
1x Ros.
2x
Roi.
4x Ros. 8x Ros. Axg.
2DGS 2h)
28.2
21.97
23.79
13.55
25.25
0.359
0.755
0.891
0.713
0.757
0.118
0.251
0.567
0.035
0.297
Clamping
28.49
0.771
0.241
0.347
0.284
(ouns)
29:30
27.16
26.10
25.77
0.577
0.795
0.235
0.215
0.329
AA-2DGG (ours) wio smoothing fitter
29.09
26.85
25.63
25.18
26.99
0.555
0.788
0.716
0.709
0.772
0.226
0.352
0.252
AA-2DGS (ouns) wio Mip filter
28.75
26,85
25.00
25.78
26.85
0.862
0.777
0.722
0,738
0.775
0.122
0.233
0.338
0:15
0.277
Table 6: Single-scale Training and Multi-scale Testing on the Mip-NeRF 360 Dataset [2). All
methods are trained on the smallest scale (1x), corresponding to eighth of the original image
resolution, and evaluated across four scales (1x, 2x, 4x, and 8x), with evaluations at higher sampling rates simulating zoom-in effects. Disabling the world-space flat smoothing filter results in high-frequency magnification artifacts when rendering higher resolution images. Disabling the 21) Mip filter causes slight decline in performance high magnification.
In order assess the effectiveness of the World-Space Flat Smoothing Kernel, we show ablation with an experiment on the single-scale training and multi-scale testing setting in the Mip-NeRF 360 dataset [ to simulate magnification or Zoom effects. We present quantitative results in Table It shows that performance degrades at higher resolution than the training one when disabling the flat smooth kernel due to high- frequnryy magnification artifacts.
In this experiment, the Object-Space Space Mip filter mostly improves results at the training resolution and does not improve much at higher ones because it is primarily designed to address aliasing in minification scenarios as we show in A.2
However, for magnification, where the rendering sampling rate exceeds the frequency content available in the trained representation, this additional filtering can sometimes lead to over-smoothing of details that would naturally become visible when zooming in, especially at extreme magnifications.
2D Gaussians are fundamentally planar primitives with zero thickness orthogonal to their surface. When viewed from grazing angles, they project to extremely thin lines on the screen, creating "needle- like" artifacts. This particularly problematic during magnification, as primitives optimized for lower resolutions suddenly reveal their orientation orientation-dependeen thinness. The flat smoothing kernel helps mitigate this issue by ensuring minimum footprint size in the tangent plane, but cannot address the fundamental zero- serccthickness property in the normal direction.
In contrast, 3D Gaussians in Mip-Splatting are volumetric primitives that maintain substantial screen presence even from oblique viewpoints. Their three-dimensional nature allows the 3D smoothing kernel effectively regularize their shape in all directions, leading to more consistent results across viewing angles and scales.
Despite these inherent limitations of planar primitives, our method still demonstrates meaningful im- provements over the original 2DGS approach. As shown in Table the combination of World-Space Flat Smoothing Kernel and Object-Space Mip Filter consistently outperforms both the clamping- based approach of the original 2DGS and the non-clamped variant.
A.2 Effectiveness of the Object-Space Mip Filter
To evaluate the effectiveness of the Object-Space Mip filter, we perform an ablation study with the single-scale training and multi-scale testing setting to simulate zoom-out effects in the Blender dataset [43. Quantitative results are shown in Table 7 Similar to previous experiments, we find that disabling the clamping heuristic performed by 2DGS (3 (2DGS w/o Clamping). the dilation artifacts are eliminated, outperforming vanilla 2DGS. However, it still shows severe aliasing artifacts especially at extreme zoom out. AA-2DGS outperforms all 2DGS variants by large gap in this
23

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PSNK C
SSIM
J
1/a Ros.
Avg.
Pill
4/4
Res.
Ros.
1/
Avg.
Rex.
1/9
Res.
1/4 Res Avp.
2XX5 86
35.055
27.64
20361
16.37
24.47
0396
03382
0.356
0.720
0384
0.033
00037
0.082
0.151 01076
2DGS w/o Champing
33.16
33.04
29.74
26.21
30.54
0.68
0.975
0.964
0.945
0.963
0.030
0.04
0,040
0.074
0.042
AA-2700S (ours)
33.34
14
13.11
29700
11
0367
0.97%
0.78
03975
0374
004
0020
0.019 0004 0.02
2DGS (ours) wio smnoothing lilter
33.38
34.08
31.96
28.84
32.06
0968
0.976
0.98
0.733
0.73
0.00
0.019 0024 0,024
AA-2KGS (ours) Mip filter
30,06
30.76
0967
0:341
Table Single-scale Training and Multi-scale Testing on the Blender Dataset [43]. All methods
are trained on full-resolution images and evaluated at four different (smaller) resolutions, with
lower resolutions simulating minification zoom-out effects. Our method achieves results that are
comparable at training resolution to 2DGS methods while significantly surpassing them at lower scales. When disabling the Object-Space Mip filter, we obtain worse results at lower scales, which shows its effectiveness in this experiment. On the other hand, disabling the world-space flat smoothing filter leads to mostly similar performance since is more involved handling magnification artifacts.
experiment. Disabling the Object-Space Mip filter results in noticeable degradation in performance, validating its important role in anti-aliasing in this minification experiment. Without the world-space flat smoothing filter, our method still produces anti-aliased rendering as the smoothing filter is designed to tackle high-frequency artifacts during magnification as shown previously.
B
Additional Qualitative Results
B.1
Additional Results for Single-scale Training and Multi-scale Testing the Blender Dataset
In this section, show additional qualitative results in Figure 6 for the minification/ Zoom Out experiments of Single-scale Training and Multi-scale Testing on the Blender Dataset
Drums
Mip-Splac.
2DGS
AA-21XIS
Mps-Splat.
20X3
Tru
Hotdog
Mic
Mip Splat.
2DGS
2D-
Mip Splat.
2DGS
Full
114
1/8
Figure 6: Additional Results of Single-scale Training and Multi-scale Testing on the Blender Dataset [43]. All methods trained at full resolution and evaluated at smaller resolutions simulate Zoom Out/ magnification. Our method (AA-2DGS) consistently demonstrates improved quality across all sampling rates compared to baseline 2DGS method.
B.2 Additional Results for Single-seale Training and Multi-scale Testing on the Mip-NeRF 360 Dataset
In this section, we show additional qualitative results in Figure 7 for the magnification/ Zoom In experiments of Single-scale Training and Multi-scale Testing the Mip-NeRF 360 Dataset [2]
24

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Mip-Splatting
2DGS
2DGS w/o Clamp.
AA-2DGS (Ours)
GT
X
Figure 7: Additional Results of Single-scale Training and Multi-scale Testing on the Mip-NeRF 360 Dataset. All models trained on 1/8 resolution and tested different upscaling factors: Bicycle (x8), Garden (x4), and Bonsai (x2). Our AA-2DGS method maintains high fidelity when rendering resolutions significantly higher than the training resolution, reducing artifacts compared to the baseline methods.
C Detailed Discussion on Limitations
While our antialiasing approach for 2D Gaussian Splatting demonstrates significant improvements over the original implementation, it is important to acknowledge certain limitations.
Balancing Antialiasing and Detail Preservation Like all antialiasing techniques, our method faces an inherent trade-off between removing aliasing artifacts and preserving fine details. We use the same filter values Mip-Splatting = 0.1 for the Object-Space Mip Filter and s = 0.2 for the World-Space Space Flat Smoothing Kernel. While these values provide good balance for most scenes, optimal parameters may vary across different datasets or viewing conditions.
Inherent Limitations of Planar Primitives A fundamental limitation stems from the nature of 2D Gaussians as planar primitives with zero thickness orthogonal to their surface. As shown in our ablation studies (Table 6). when viewed from grazing angles, these primitives project to extremely thin lines on the screen, creating "needle-like" artifacts. This is particularly problematic during extreme magnification, primitives optimized for lower resolutions suddenly reveal their orientation-dependeer thinness.
The World-Space Flat Smoothing Kernel helps mitigate this issue by ensuring a minimum footprint size in the tangent plane, but cannot address the fundamental zero-thickness property in the normal direction. In contrast, volumetric primitives like those used in 3D Gaussian Splatting maintain substantial screen presence even from oblique viewpoints, allowing their smoothing kernels to regularize shape in all directions.
Magnification and Minification Trade-offs As demonstrated our ablation studies, the Object- Space Mip Filter primarily addresses aliasing in minification scenarios (zoom-out), while the World- Space Flat Smoothing Kernel targets high-frequency artifacts during magnification (zoom-in). For extreme magnification cases where the rendering sampling rate exceeds the frequency content available in the trained representation, our filtering approach can sometimes lead to over-smoothing of details that would naturally become visible when zooming in.
Despite these limitations, our experiments consistently show that the combination of World-Space Flat Smoothing Kernel and Object-Space Mip Filter outperforms both the clamping-base based approach
25

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of the original 2DGS and non-clampae clamped variants, particularly in challenging multi-scale rendering scenarios. Our method provides more principled approach to antialiasing for 2D Gaussian Splatting while maintaining the computational efficiency and view-consistent geometry that makes 2DGS attractive.
26


Reviews:
[{"review_id": "2PgCfRfcxU", "reviewer": "Reviewer_MET9", "summary": "This paper takes insight from Mip-Splatting to bridge the anti aliasing gap between 2dgs and 3dgs. Although 2dgs has better geometry fidelity the clamping method usually leads to the loss of high frequency details. In this paper a smoothing kernel is designed based on the sampling rate of the training views as well as adapting the mip filter to the local space of each splat. \n\nThe results show significant improvement upon 2DGS while being compareable agianst mipsplatting in most scenarios.", "questions": "How do you compare against hdgs?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\nEffective: The core contribution of mitigating aliasing artifacts in 2DGS is highly successful, as evidenced by the significant quantitative improvements against 2DGS across various multi-scale rendering tests (Tables 1, 2, and 3) and compelling qualitative results (Figures 1 and 3). This directly addresses a critical limitation of 2DGS, enhancing its practical applicability.\n\nComputational Efficiency: The paper states that it incurs \"a slight overhead of 10-15% in rendering time compared to the original 2DGS,\" which is a reasonable trade-off for the notable quality improvement. This efficiency ensures the method remains viable for real-time applications.\n\nPreservation of Geometric Fidelity: A key advantage of 2DGS is its superior geometric accuracy. The paper demonstrates that AA-2DGS maintains these capabilities, showing comparable or slightly improved reconstruction performance on the DTU dataset (Table 4), indicating that the anti-aliasing mechanisms do not compromise the underlying geometric representation.\n\nWell-Written and Clear: The paper is well-structured and clearly explains the technical challenges and the proposed solutions. The methodology, particularly the derivation of the object-space Mip filter, is presented in a comprehensible manner.\n\nWeakness:\nIncremental Novelty: While highly effective and well-executed, the primary novelty of this work lies in adapting and integrating existing anti-aliasing principles (specifically from mip-splatting and EWA filtering) into the 2Dgs framework. The conceptual methods are largely derived from prior work rather than introducing fundamentally new algorithmic approaches. \n\nnit: line 277: wrong reference ?? , line 278: Such should be lower cased.", "was_revised": true, "final_justification": "I have read the author's reply regarding the specifics of the contributions in this work and other reviews similar concern about the novelty. Given that the steps are crucial for improving 2dgs and results show significant improvement while not adding much overhead I believe the slight lack of novelty is not a justification for rejection of this paper. You don't always need to reinvent the wheel from scratch. Hence maintaining my rating as borderline accept.", "rebuttal": "We thank the reviewer for their positive and constructive feedback. We are pleased that they found our method to be \"highly successful\", \"computationally efficient\" and \"well-written\". We appreciate the opportunity to address the remaining concerns regarding novelty and the comparison to HDGS.\n\n### On Novelty and Contribution\n\nWe respectfully wish to clarify that our work introduces a novel algorithmic framework derived from a signal-processing analysis of the unique 2DGS rendering pipeline and the planar nature of its primitives. This required deriving a first-of-its-kind Object-Space Mip Filter and a non-trivial adaptation of band-limiting for planar primitives with the World-Space Flat Smoothing Kernel, as direct adaptation of prior art is not possible.\n\n\n**1. A Novel Framework is Required for the 2DGS Rendering Pipeline.**\nThe rendering mechanisms of 3DGS and 2DGS are fundamentally incompatible, precluding any direct application of 3DGS-based solutions.\n*   **3DGS** projects 3D Gaussians to screen space, which is a well-defined affine transformation. Applying screen-space EWA filtering is a direct extension of existing literature.\n*   **2DGS** does not project primitives. Instead, it computes a non-linear ray-splat intersection (Eq. 5 and 6) to find a point in the planar splat’s local 2D space.\n\nIt is not at all trivial how to perform frequency-based band-limiting or pre-filtering in this context. Our \"Object-Space Mip Filter\" is a novel solution tailored specifically for this unique intersection-based rendering paradigm. **It is a new mathematical derivation with broader impact.**\n\nThe reviewer notes that our work adapts existing principles. However, our technical approach is fundamentally different and more novel than that of Mip-Splatting. Mip-Splatting uses the standard screen-space EWA formulation and simply modifies the filter kernel size.\n\nIn contrast, our work introduces a new mathematical derivation for an anti-aliasing filter that operates directly in the object space of each splat.\n\nThe derivation proceeds as follows:\n*   **The Mapping:** We begin with the ray-splat intersection function $m(\\mathbf{x}) = \\mathbf{u}(\\mathbf{x})$ on the splat (Eq. 5 and 6).\n*   **The Core Innovation:** We form a first-order Taylor approximation of this complex mapping around a pixel, defined by its Jacobian $\\mathbf{J} = \\frac{\\partial \\mathbf{u}}{\\partial \\mathbf{x}}$ (Eq. 12). This local affine approximation is the key insight that makes an analytical solution possible.\n*   **Mapping to Screen Space:** Using this affine mapping and a fundamental property of Gaussian functions, we can express how the local unit Gaussian behaves in screen space (Eq. 13).\n*   **Filtering in Screen Space:** We then mathematically convolve this transformed Gaussian with a screen-space Mip filter (a Gaussian with covariance $\\sigma\\mathbf{I}$) to obtain a filtered Gaussian with covariance $\\mathbf{J}^{-1}\\mathbf{J}^{-\\top} + \\sigma\\mathbf{I}$ (Eq. 15).\n*   **Mapping Back to Object Space:** Finally, we use the same affine transform properties to map this filtered screen-space Gaussian back into the splat's local `uv`-space. This yields our final, novel result: the primitive can be evaluated as a single Gaussian in its local space with a new, dynamically computed covariance $\\boldsymbol{\\Sigma}_{\\text{local}}' = \\mathbf{I} + \\sigma\\mathbf{J}\\mathbf{J}^\\top$ (Eq. 17).\n\nTo our knowledge, this formulation—pre-filtering an intersection-based primitive by analytically deriving an equivalent object-space covariance from a screen-space filter—is a new contribution that has not been proposed before, not even in classical graphics literature on surface splatting, highlighting its novelty and potential impact beyond just 2DGS.\n\n**2. The World-Space Flat Smoothing Kernel: A Necessary and Non-Trivial Formulation for Planar Primitives.**\n\nOur second component addresses pre-aliasing by band-limiting the representation, conceptually inspired by the frequency analysis in Mip-Splatting. However, a 3D volumetric filter cannot be applied to a 2D planar primitive. Our contribution was to correctly formulate this regularization for a flattened representation by projecting the 3D isotropic smoothing kernel onto the 2D splat plane. This results in our effective planar covariance matrix ($\\mathbf{V}_k^{\\text{eff}}$ in Eq. 9) and corresponding opacity modulation (Eq. 10), which is a necessary step to complete the anti-aliasing framework for 2DGS, especially for the case of magnification.\n\n**3. We provide new insights into the limitations of the original 2DGS.**\n\nA key contribution of our work is the analysis and demonstration of the clamping heuristic's ineffectiveness in the original 2DGS. Prior work had not identified this as a critical source of artifacts. Our experiments (2DGS w/o Clamping in Tables 1-5, and in the Appendix) provides the first concrete evidence that this heuristic often exacerbates aliasing and damages performance, especially during minification. This insight itself is a valuable contribution to the understanding of the vanilla 2DGS method.\n\n### Regarding the Comparison to HDGS\n\nWe thank the reviewer for bringing HDGS to our attention. We are happy to provide a direct comparison based on the results presented in both papers.\n\nThe HDGS paper includes a \"Reduced resolution rendering on the NeRF synthetic dataset\" experiment (Table 1 in HDGS), which directly corresponds to our \"Single-scale Training and Multi-scale Testing on the Blender Dataset\" experiment (Table 2 in our submission). A direct comparison of the average PSNR demonstrates the superiority of our approach:\n*   **Our Method (AA-2DGS):** 32.11 PSNR\n*   **HDGS:** 29.45 PSNR\n\nOur method significantly outperforms HDGS on this benchmark by over 2.6 dB. We believe this substantial performance gap stems from the fundamental difference in our anti-aliasing strategies. HDGS employs a frustum-based super-sampling method. It treats each pixel as a frustum and casts 5 rays to average the result which is approximative and computationally more expensive. Differently, our method uses a principled analytical pre-filtering approach that mitigates aliasing more effectively and efficiently by directly addressing its signal-processing roots.\n\nFinally, we will gladly correct any typos and formatting issues in the final version.\n\nWe hope these clarifications have addressed the reviewer's concerns and reinforced the novelty and significance of our contributions. We thank the reviewer again for their valuable time and feedback."}, {"review_id": "gO3anxfFqo", "reviewer": "Reviewer_oTXT", "summary": "The paper extends mip-splatting to 2D Gaussian Splatting (GS) representations. It introduces a world-space flat smoothing kernel that constrains the frequency content of 2D Gaussian primitives based on the maximum sampling frequency of the training views. Additionally, the authors derive an object-space mipmap filter by leveraging an affine approximation of the ray–splat intersection mapping, which enables efficient and accurate anti-aliasing directly in the local space of each splat. Quantitative evaluations show performance comparable to mip-splatting.", "questions": "Addressing points 1-2 in weakness is helpful", "limitations": "missing discussion of limitations.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**Strength**\nThe paper is well-written and addresses an important problem: extending anti-aliasing to 2D Gaussian Splatting representations. This has the potential for significant impact given the widespread use of 2D GS. The derivation is clearly presented, and the results demonstrate state-of-the-art performance for both training and rendering across multiple resolutions.\n\n**Weakness (Minor)**\n1. The paper lacks a discussion of its limitations, which would help contextualize the scope and applicability of the proposed method.\n2. Including additional visualizations beyond RGB—such as meshes, surface normals, and depth—would strengthen the qualitative analysis. These are particularly relevant since improved geometric modeling is a key motivation for adopting 2D GS.\n3. There are broken links in Line 277", "was_revised": true, "final_justification": "I have read the rebuttal and the feedback from other reviewers. Shared concerns include the novelty of the approach and the lack of comparison and justification regarding geometric accuracy. I also agree that the formulation appears generally similar to Mip-NeRF’s splatting, but I acknowledge the practical value of adapting Mip-splatting to 2D Gaussian splatting. Addressing aliasing is an important component, especially if the community remains interested in leveraging 2D-GS. Given this, I tend to maintain my positive rating.", "rebuttal": "We sincerely thank the reviewer for their time and for providing a thoughtful and positive assessment of our work. We are grateful for the \"excellent\" ratings in Quality, Clarity, and Significance and for the recognition that our paper addresses an important problem with a clear derivation and state-of-the-art results.\n\nWe would like to address the minor weaknesses pointed out by the reviewer, which we believe will further strengthen our paper.\n\n1.  **Regarding the Discussion of Limitations:**\n\n    The reviewer noted that \"The paper lacks a discussion of its limitations.\" We would like to respectfully clarify that we included a dedicated **\"Limitations\" section (Section C) in the Appendix**.\n\n    In this section, we discuss:\n    *   The inherent trade-off between antialiasing and preserving high-frequency detail, a common challenge for all such methods.\n    *   The fundamental limitations stemming from the use of 2D planar primitives, including the \"needle-like\" artifacts that can appear at grazing angles during magnification.\n    *   The specific trade-offs of our two-part solution, where the Object-Space Mip filter primarily addresses minification (zoom-out) and the Flat Smoothing Kernel targets magnification (zoom-in).\n\n    We acknowledge that this section is in the appendix, which may have been overlooked due to this placement. We believe this discussion thoroughly contextualizes the scope and applicability of our method as requested.\n\n2.  **Regarding Additional Visualizations (Depth, Normals, Meshes):**\n\n    We thank the reviewer for this constructive suggestion. Our evaluation was centered on showcasing the primary contributions: significant improvements in anti-aliased rendering at multiple scales, while numerically demonstrating the preservation of 2DGS's geometric fidelity on the DTU benchmark. We will happily include additional visualizations in the revision. As the reviewer intuits, the benefits of our anti-aliasing are not confined to RGB output but naturally extend to all rendered buffers."}, {"review_id": "H6NuxMNDhb", "reviewer": "Reviewer_4K14", "summary": "This paper address the aliasing issues in 2D Gaussian Splatting. It adapted the idea from Mip-Splatting to 2DGS. Specifically, it projects the 3D smoothing filter in mip-splatting to the 2D Gaussian plane and apply the smoothing filter. It also unprojects the screen space 2D mip filter in mip-splatting to 2D Gaussian primitives' object space and apply the filter. The proposed method is valid in several datasets and shows anti-aliased results compared to 2DGS and the final results is comparable to mip-splatting.", "questions": "2DGS has surface reconstruction results on the tanks and temples datasets and I wonder if the anti-aliased solution also improve the resurface reconstruction those scenes.", "limitations": "I don't see significant limitations.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The motivation and solution is clear. 2DGS has aliasing issues due to max operator and mip-splatting's idea could be used to improve the results.\n2. The writing is clear.\n3. The proposed idea is validated in several datasets and experiments supported the claims. \n\nWeakness:\n1. The proposed idea is a straightforward adaptation of mip-splatting to 2DGS.\n2. The results are not surprising as mip-splatting's solutions for anti-aliasing are very general.", "was_revised": true, "final_justification": "After reading the rebuttal and other reviews, I think the paper nicely adapt the idea of mip-splatting to 2DGS to address the aliasing issues and I will maintain my rating as borderline accept.", "rebuttal": "We sincerely thank the reviewer for their time and valuable feedback. We are pleased they found our paper’s motivation and writing to be clear and appreciate their positive assessment of our experimental validation. We would like to address the concerns raised regarding the work's originality and the scope of our evaluation to provide a clearer picture of our contributions.\n\n### On the Novelty of our Method Beyond a \"Straightforward Adaptation\"\n\nWe appreciate the reviewer's recognition that our work is inspired by Mip-Splatting. However, we wish to clarify that our method is not a \"straightforward adaptation\" but involves significant novel mathematical derivations necessitated by the fundamental rendering paradigm differences between 3DGS and 2DGS.\n\n**1. A Novel Framework is Required for the 2DGS Rendering Pipeline.**\nThe rendering mechanisms of 3DGS and 2DGS are fundamentally incompatible, precluding any direct application of 3DGS-based solutions.\n*   **3DGS** projects 3D Gaussians to screen space, which is a well-defined affine transformation. Applying screen-space EWA filtering is a direct extension of existing literature.\n*   **2DGS** does not project primitives. Instead, it computes a complex, non-linear ray-splat intersection (Eq. 5 and 6) to find a point in the planar splat’s local 2D space.\n\nIt is not at all trivial how to perform frequency-based band-limiting or pre-filtering in this context. Our \"Object-Space Mip Filter\" is a novel solution tailored specifically for this unique intersection-based rendering paradigm. **It is a new mathematical derivation with broader impact.**\n\nThe reviewer notes that our method is \"straightforward adaptation of mip-splatting\". However, our technical approach is fundamentally different and more novel than that of Mip-Splatting. Mip-Splatting uses the standard screen-space EWA formulation and simply modifies the filter kernel size.\n\nIn contrast, our work introduces a new mathematical derivation for an anti-aliasing filter that operates directly in the object space of each splat.\nThe derivation proceeds as follows:\n*   **The Mapping:** We begin with the ray-splat intersection function $m(\\mathbf{x}) = \\mathbf{u}(\\mathbf{x})$ on the splat (Eq. 5 and 6).\n*   **The Core Innovation:** We form a first-order Taylor approximation of this complex mapping around a pixel, defined by its Jacobian $\\mathbf{J} = \\frac{\\partial \\mathbf{u}}{\\partial \\mathbf{x}}$ (Eq. 12). This local affine approximation is the key insight that makes an analytical solution possible.\n*   **Mapping to Screen Space:** Using this affine mapping and a fundamental property of Gaussian functions, we can express how the local unit Gaussian behaves in screen space (Eq. 13).\n*   **Filtering in Screen Space:** We then mathematically convolve this transformed Gaussian with a screen-space Mip filter (a Gaussian with covariance $\\sigma\\mathbf{I}$) to obtain a filtered Gaussian with covariance $\\mathbf{J}^{-1}\\mathbf{J}^{-\\top} + \\sigma\\mathbf{I}$ (Eq. 15).\n*   **Mapping Back to Object Space:** Finally, we use the same affine transform properties to map this filtered screen-space Gaussian back into the splat's local `uv`-space. This yields our final, novel result: the primitive can be evaluated as a single Gaussian in its local space with a new, dynamically computed covariance $\\boldsymbol{\\Sigma}_{\\text{local}}' = \\mathbf{I} + \\sigma\\mathbf{J}\\mathbf{J}^\\top$ (Eq. 17).\n\nTo our knowledge, this formulation—pre-filtering an intersection-based primitive by analytically deriving an equivalent object-space covariance from a screen-space filter—is a new contribution that has not been proposed before, not even in classical graphics literature on surface splatting, highlighting its novelty and potential impact beyond just 2DGS.\n\n**2. The World-Space Flat Smoothing Kernel: A Necessary and Non-Trivial Formulation for Planar Primitives.**\n\nOur second component addresses pre-aliasing by band-limiting the representation, conceptually inspired by the frequency analysis in Mip-Splatting. However, a 3D volumetric filter cannot be applied to a 2D planar primitive. Our contribution was to correctly formulate this regularization for a flattened representation by projecting the 3D isotropic smoothing kernel onto the 2D splat plane. This results in our effective planar covariance matrix ($\\mathbf{V}_k^{\\text{eff}}$ in Eq. 9) and corresponding opacity modulation (Eq. 10), which is a necessary step to complete the anti-aliasing framework for 2DGS, especially for the case of magnification.\n\n**3. We provide new insights into the limitations of the original 2DGS.**\n\nA key contribution of our work is the analysis and demonstration of the clamping heuristic's ineffectiveness in the original 2DGS. Prior work had not identified this as a critical source of artifacts. Our experiments (2DGS w/o Clamping in Tables 1-5, and in the Appendix) provides the first concrete evidence that this heuristic often exacerbates aliasing and damages performance, especially during minification. This insight itself is a valuable contribution to the understanding of the vanilla 2DGS method.\n\n### On the Surface Reconstruction Evaluation\n\nWe thank the reviewer for their question regarding surface reconstruction on other datasets. We would like to gently point to **Section 4.5 and Table 4**, where we evaluate our method on the standard DTU surface reconstruction benchmark precisely to address this concern.\n\nThe purpose of this experiment was to demonstrate that our anti-aliasing solution preserves the state-of-the-art geometric accuracy that is a major strength of 2DGS. The results in Table 4, showing our method is on par with the original 2DGS confirm that our framework successfully integrates advanced anti-aliasing without degrading this crucial capability. We believe the comprehensive DTU benchmark is sufficient to robustly support this claim.\n\nOur paper introduces a novel, non-trivial method for 2DGS anti-aliasing, provides new insights into the limitations of the original 2DGS, and validates that our approach preserves its key geometric strengths. We hope these clarifications help the reviewer better appreciate the originality and significance of our contributions.\n\nThank you again for your time and consideration."}, {"review_id": "1b3ZHC05tb", "reviewer": "Reviewer_EnMS", "summary": "In this paper, the authors introduce Anti-Aliased 2D Gaussian Splatting (AA-2DGS) by identifying two key insights: 1) first, they impose a **world-space flat smoothing kernel** that constrains the frequency content of each 2D Gaussian primitive based on the maximal sampling rate of the training views, effectively eliminating high-frequency artifacts when zooming in; 2) second, they derive an **object-space Mip filter** via an affine approximation of the ray-splat intersection mapping, allowing proper anti-aliasing to be applied directly in each splat’s local coordinate frame. \n\nBy integrating these mechanisms, AA-2DGS mitigates aliasing under both zoom-in and zoom-out conditions while preserving the precise geometry that makes 2DGS valuable. Experimental results show that AA-2DGS consistently outperforms vanilla 2DGS and rivals state-of-the-art anti-aliased 3DGS methods in PSNR, SSIM, and LPIPS across all resolutions.", "questions": "- In Table 5, I notice comparisons with Zip-NeRF and Mip-NeRF 360. However, those two methods use different image resolutions than vanilla 3D-GS (Mip-NeRF 360 downsamples outdoor scenes by $4x$ and indoor scenes by $2x$, whereas vanilla 3D-GS uniformly crops the width to 1600 pixels). Did the authors adopt the same resolution settings across all methods? Such differences can significantly impact the reported rendering metrics.", "limitations": "Yes.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- This paper is well-written and easy to follow.\n- Decent results for anti-aliasing and geometry reconstruction.\n\nWeaknesses:\n\n- Missing references for dynamic reconstruction based on 3D-GS:\n\n  - Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting, by Zeyu Yang et al. ICLR 2024. The first 4D Gaussian splatting.\n  - Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction, by Ziyi Yang et al. CVPR 2024. The first deformable Gaussian splatting.\n\n  AbsGS (AbsGS: Recovering Fine Details for 3D Gaussian Splatting) should also be cited under the methods to improve density control in Line 91.\n\n- **Lack of novelty**. The paper’s contribution is largely one of adaptation rather than innovation. It essentially ports Mip-Splatting to the 2D-GS framework without proposing new smoothing strategies. The authors transform the 3D Gaussian low-pass filter into a planar, world-space “flat” smoothing kernel and change the screen-space Mip filter into each splat’s local coordinate frame.\n\n- I find the stated purpose of AA-2DGS somewhat unclear. Had the authors concentrated their contributions on improving the mesh quality produced by 2D Gaussian Splatting, I would rate the paper more highly. Instead, they place the bulk of their emphasis on anti-aliasing. It remains unclear what inherent advantage 2DGS holds over 3DGS in addressing aliasing: after all, 2DGS is merely a specialized case of Gaussian splatting tailored for geometry reconstruction, and subsequent works have shown that for pure rendering tasks, 2DGS offers no substantial benefit over its 3D counterpart.\n\n- **Missing qualitative results on mesh.** The Experiments section omits any qualitative visualizations of the reconstructed mesh. Since AA-2DGS is fundamentally an anti-aliasing extension of 2DGS, the paper would benefit from showing rendered mesh outputs to illustrate geometric improvements. As it stands, only Table 4 reports reconstruction metrics, with no accompanying mesh visual examples.\n\n- Missing comparison on FPS.", "was_revised": true, "final_justification": "I still feel that the authors somewhat overstate the significance of their contributions. At its core, the work primarily involves adapting mip-splatting to 2DGS, with the necessary modifications for that transition. Additionally, I remain unconvinced by the motivation behind AA-2DGS, as I believe anti-aliasing and geometry reconstruction are fundamentally unrelated. The experimental results further suggest that AA-2DGS performs worse in multi-scale rendering compared to 2DGS and shows inferior geometry reconstruction compared to state-of-the-art methods based on 2DGS. While I will raise the score based on the authors’ rebuttal, I still lean toward a weak rejection. However, I am open to accepting the paper if deemed appropriate.", "rebuttal": "We thank the reviewer for their time and feedback. We appreciate their positive comments on the paper's clarity, results, and organization. We will correct any missing references, formatting issues, or other minor points in the final version of the paper.\n\nWe would like to address the main weaknesses and questions raised, as we believe they stem from a few key misunderstandings of our work's motivation and technical novelty.\n\n### On the Purpose and Significance of Anti-Aliasing for 2DGS\n\nThe reviewer questions the purpose of our work, suggesting we should have focused on improving mesh quality and that \"2DGS offers no substantial benefit over its 3D counterpart\" for rendering. We are afraid this reflects a misunderstanding of our central motivation.\n\nWe would like to clarify a key point regarding the paper's goal for geometry, which appears to have been misinterpreted. The central claim is one of **preservation, not improvement**.\n\nOur work is motivated by the fact that 2DGS possesses established geometric advantages over 3DGS, but its rendering limitations due to aliasing hinder its wider practical use and adoption. Therefore, our primary objective was to design an anti-aliasing solution that could be integrated into the pipeline without degrading this geometric fidelity. The DTU experiment (Table 4) was undertaken specifically to validate this claim of preservation. The results, showing our method performing on par with the strong baseline, should thus be interpreted as a success: we fixed the rendering problems while maintaining the geometric strengths.\n\n2DGS is a vital and active research direction precisely because of its geometric accuracy. As we state in the introduction, 2DGS provides superior depth and normal reconstruction compared to 3DGS. This has led to a recent surge in follow-up work that builds upon the 2DGS representation for tasks where geometry is paramount and integral to rendering, such as in reflective scene modeling or physics based rendering where rendering is dependent on reflection direction (ie normal), e.g.:\n*   *Ref-GS: Directional Factorization for 2D Gaussian Splatting*. CVPR 25\n*   *Ref-Gaussian Reflective Gaussian Splatting*. ICLR 25\n\n, sparse-view joint reconstruction and novel view synthesis, e.g.:\n*   *MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views*, CVPR 25\n\n, physics-based inverse rendering with ray tracing, e.g.:\n*   *IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing*, CVPR 25\n\nAn anti-aliased 2DGS representation is highly impactful and beneficial for all such applications of 2DGS.\n\n*   **The primary limitation of 2DGS is aliasing.** Its inability to render well at different scales (e.g., camera zoom) prevents its widespread adoption despite its geometric advantages. Our work directly addresses this bottleneck.\n*   **Our aim is a unified representation.** We seek to create a single, robust representation that excels at both high-quality, multi-scale rendering and accurate surface reconstruction from a single training, combining the best of both worlds. The experiments were therefore designed to show that we significantly improve rendering while maintaining the state-of-the-art geometric performance of 2DGS.\n\nThis was also agreed on by other reviewers:\n> **MET9** “This directly addresses a critical limitation of 2DGS, enhancing its practical applicability.”\n\n> **oTXT** “The paper is well-written and addresses an important problem: extending anti-aliasing to 2D Gaussian Splatting representations. This has the potential for significant impact given the widespread use of 2D GS.”\n\n> **4K14** “The motivation and solution is clear. 2DGS has aliasing issues due to max operator”\n\n### On Novelty: Beyond a Simple Adaptation of Mip-Splatting\n\nThe reviewer characterizes our work as a simple adaptation of Mip-Splatting. We respectfully disagree and wish to highlight our novel technical contributions. Our framework consists of two distinct components, each addressing a different aspect of aliasing with a different level of innovation.\n\n**1. A Novel Framework is Required for the 2DGS Rendering Pipeline.**\nThe rendering mechanisms of 3DGS and 2DGS are fundamentally incompatible, precluding any direct application of 3DGS-based solutions.\n*   **3DGS** projects 3D Gaussians to screen space, which is a well-defined affine transformation. Applying screen-space EWA filtering is a direct extension of existing literature.\n*   **2DGS** does not project primitives. Instead, it computes a complex, non-linear ray-splat intersection (Eq. 5 and 6) to find a point in the planar splat’s local 2D space.\n\nIt is not at all trivial how to perform frequency-based band-limiting or pre-filtering in this context. Our \"Object-Space Mip Filter\" is a novel solution tailored specifically for this unique intersection-based rendering paradigm. **It is a new mathematical derivation with broader impact.**\n\nOur technical approach is fundamentally different and more novel than that of Mip-Splatting. Mip-Splatting uses the standard screen-space EWA formulation and simply modifies the filter kernel size.\n\nIn contrast, our work introduces a new mathematical derivation for an anti-aliasing filter that operates directly in the object space of each splat.\nThe derivation proceeds as follows:\n*   **The Mapping:** We begin with the ray-splat intersection function $m(\\mathbf{x}) = \\mathbf{u}(\\mathbf{x})$ on the splat (Eq. 5 and 6).\n*   **The Core Innovation:** We form a first-order Taylor approximation of this complex mapping around a pixel, defined by its Jacobian $\\mathbf{J} = \\frac{\\partial \\mathbf{u}}{\\partial \\mathbf{x}}$ (Eq. 12). This local affine approximation is the key insight that makes an analytical solution possible.\n*   **Mapping to Screen Space:** Using this affine mapping and a fundamental property of Gaussian functions, we can express how the local unit Gaussian behaves in screen space (Eq. 13).\n*   **Filtering in Screen Space:** We then mathematically convolve this transformed Gaussian with a screen-space Mip filter (a Gaussian with covariance $\\sigma\\mathbf{I}$) to obtain a filtered Gaussian with covariance $\\mathbf{J}^{-1}\\mathbf{J}^{-\\top} + \\sigma\\mathbf{I}$ (Eq. 15).\n*   **Mapping Back to Object Space:** Finally, we use the same affine transform properties to map this filtered screen-space Gaussian back into the splat's local `uv`-space. This yields our final, novel result: the primitive can be evaluated as a single Gaussian in its local space with a new, dynamically computed covariance $\\boldsymbol{\\Sigma}_{\\text{local}}' = \\mathbf{I} + \\sigma\\mathbf{J}\\mathbf{J}^\\top$ (Eq. 17).\n\nTo our knowledge, this formulation—pre-filtering an intersection-based primitive by analytically deriving an equivalent object-space covariance from a screen-space filter—is a new contribution that has not been proposed before, not even in classical graphics literature on **surface splatting**, highlighting its novelty and potential impact beyond just 2DGS.\n\n**2. The World-Space Flat Smoothing Kernel: A Necessary and Non-Trivial Formulation for Planar Primitives.**\n\nOur second component addresses pre-aliasing by band-limiting the representation, conceptually inspired by the frequency analysis in Mip-Splatting. However, a 3D volumetric filter cannot be applied to a 2D planar primitive. Our contribution was to correctly formulate this regularization for a flattened representation by projecting the 3D isotropic smoothing kernel onto the 2D splat plane. This results in our effective planar covariance matrix ($\\mathbf{V}_k^{\\text{eff}}$ in Eq. 9) and corresponding opacity modulation (Eq. 10), which is a necessary step to complete the anti-aliasing framework for 2DGS, especially for the case of magnification.\n\n**3. We provide new insights into the limitations of the original 2DGS.**\n\nA key contribution of our work is the analysis and demonstration of the clamping heuristic's ineffectiveness in the original 2DGS. Prior work had not identified this as a critical source of artifacts. Our experiments (2DGS w/o Clamping in Tables 1-5, and in the Appendix) provides the first concrete evidence that this heuristic often exacerbates aliasing and damages performance, especially during minification. This insight itself is a valuable contribution to the understanding of the vanilla 2DGS method.\n\n### On Missing Qualitative Mesh Results & FPS Comparison\n\n**Mesh Visualizations:** Our initial focus on quantitative metrics in Table 4 was to rigorously demonstrate that our method preserves the state-of-the-art geometric accuracy of 2DGS, which was our central claim for this aspect of our work. We deemed numerical results sufficient as they clearly show preservation of baseline performance. To the request of the reviewer, we will add mesh visualizations.\n\n**FPS Comparison:** The reviewer states that a comparison on FPS is missing. **This is a factual error.** We explicitly report the overhead in Section 4.1, \"Implementation Details\":\n> \"our approach incurs a slight overhead of 10-15% in rendering time compared to the original 2DGS implementation.\"\n\n### On Experimental Settings (Table 5)\n\nThe reviewer correctly notes that different methods on the Mip-NeRF 360 benchmark use specific resolution settings. We confirm we followed the standard protocol. As stated in Section 4.3 (Single-scale Training and Same-scale Testing):\n> \"Indoor scenes are downsampled by a factor of 2 and outdoor by 4.\"\n\nThe reported numbers for other methods are taken from Mip-Splatting, which used the same standard setup, thus ensuring a fair comparison.\n\nWe hope these clarifications address the reviewer's concerns and underscore the novelty and significance of our contributions. We are confident that AA-2DGS is a valuable step forward in making a geometrically superior representation practical for high-quality, multi-scale rendering. We thank the reviewer again for their constructive feedback."}]

Author rebuttal:

Meta-review: The final recommendations of the expert reviewers are 2x Borderline accept, 1x Accept, and 1x Borderline reject. These recommendations are made after addressing the authors' feedback and through a thorough discussion. This paper presents a framework to bridge the antialiasing gap between 2D Gaussian Splatting (2DGS) and 3D Gaussian Splatting (3DGS), drawing key inspiration from Mip-Splatting. The core contribution lies in the derivation of an object-space mipmap filter through an affine approximation of the ray-splat intersection mapping. This enables efficient and accurate antialiasing directly within the local coordinate space of each splat, offering a principled approach to addressing artifacts that are particularly prominent in 2DGS. The technical approach is well-motivated, addressing a known and important problem in 3DGS. The authors present compelling visual results and quantitative comparisons that demonstrate improved image quality compared to the baseline 2DGS.

However, all the reviewers raise concerns regarding the perceived novelty and technical depth of the contribution. The reviewers are not fully convinced that the work is more than a straightforward application of the principles from Mip-Splatting to the 2DGS framework. The “why” and “how” of the proposed object-space affine approximation, and what makes it a non-trivial step, are not sufficiently explained. The reviewers have also pointed out that the geometric accuracy is insufficient, which is a known strength of 2DGS. In addition, the discussion of limitations is misplaced within the paper’s structure and should be moved to a more standard position (e.g., before the conclusion).

The Area Chair concurs with the reviewers that the concerns raised are valid and must be addressed for the paper to realize its full potential. Despite these concerns, the Area Chair believes the paper makes a valuable contribution to the field of Gaussian-Splatting-based techniques. Given the overall strength of the method and the thoughtful response to reviewer feedback, the consensus supports acceptance.
Decision: Accept (poster)