In rendering, a camera is a sampling operator that maps each finite pixel to a bundle of rays. Different camera models change the geometry of this bundle, thus making a unified and faithful rendering formulation challenging. Consequently, Gaussian ray tracing supports generic cameras (with optical center) through their inverse ray mappings, yet typically reduces every pixel to a single center ray. This ignores the camera-dependent pixel footprint, causing aliasing under minification, while unconstrained Gaussians expose unsupported frequencies under magnification. We present ConeGaussian, a camera-model-agnostic anti-aliasing framework for Gaussian ray-based rendering. Instead of defining the pixel filter on a camera-specific image plane, ConeGaussian constructs an anisotropic footprint directly from neighboring rays produced by the camera's native inverse mapping. We derive a closed-form response under a locally linear, depth-local, moment-matched approximation of the finite pixel footprint, while the same geometry defines a per-Gaussian training-frequency floor. Notably, by construction, our filtering principle can be used unmodified across calibrated central camera models and multiple Gaussian ray-rendering backbones. Additionally, unlike in mip-splatting, our scene-space frequency floor and filtering enable trivial composition at render time, allowing us to remove excess blurring. On pinhole and strongly distorted fisheye captures, ConeGaussian consistently improves two distinct ray-based backbones, by up to 4.3 dB at 1/8 resolution, and reduces fisheye LPIPS by 30% where perspective screen-plane footprint formulations are not directly applicable.
Figures & tables
Figure 1: ConeGaussian suppresses aliasing at 81 minification and preserves detail at ×8 magnification for both pinhole and fisheye cameras.
Figure 2: The ConeGaussian pixel-footprint filter. (a) Pixel-edge ray differentials form a world-space footprint at the maximum-response depth t∗ . (b) Whitening yields an anisotropic rank-two covariance; ConeGaussian preserves its orientation and aspect ratio, unlike single-ray 3DGEER ( Huang et al. 2025 ) and isotropic VKRayGS-style filtering ( Rota Bulò et al. 2025 ) . (c,d) Against a 32×32 supersampled reference, the anisotropic filter achieves the lowest response error for both pinhole and fisheye cameras, especially off-axis. Beyond this single-Gaussian response test, we also validate multi-Gaussian occlusion compositing against dense sub-ray references; see the Appendix for details.
Dataset
Method
1
21
41
81
Avg.
ScanNet++ [1pt]fisheye
3DGUT
29.33 / .915 / .248
30.38 / .934 / .193
31.47 / .954 / .112
30.24 / .953 / .078
30.36 / .939 / .158
ConeGaussian w. 3DGUT
29.67 / .921 / .239
30.61 / .938 / .185
31.66 / .956 / .108
31.65 / .963 / .059
30.90 / .944 / .148
3DGEER
27.59 / .911 / .257
28.53 / .929 / .202
28.53 / .946 / .123
28.02 / .946 / .084
28.17 / .933 / .167
ConeGaussian w. 3DGEER
27.65 / .917 / .243
28.29 / .931 / .189
29.27 / .950 / .111
29.93 / .962 / .052
28.78 / .940 / .149
Zip-NeRF [1pt]fisheye
3DGUT
23.40 / .781 / .413
24.18 / .835 / .299
24.73 / .867 / .192
24.58 / .865 / .147
24.22 / .837 / .263
ConeGaussian w. 3DGUT
23.48 / .791 / .403
24.22 / .840 / .292
24.82 / .871 / .185
25.14 / .880 / .128
24.42 / .845 / .252
Table 1: MTMT (PSNR ↑ /SSIM ↑ /LPIPS ↓ ). Models are trained and evaluated at {1,21,41,81} . Gray rows are author-reported screen-space pinhole-specialized baselines that do not support generic cameras ( Yu et al. 2024a ; Liang et al. 2024 ) (reported here only for references). Green / orange denote the best/second-best generalizable ray-based methods per column and metric.
Dataset
Method
1
21
41
81
Avg.
ScanNet++ [1pt]fisheye
3DGEER (baseline)
27.89 / .921 / .243
28.18 / .930 / .198
27.10 / .930 / .155
26.58 / .918 / .131
27.44 / .925 / .182
VKRayGS z/f
26.20 / .919 / .244
25.89 / .922 / .205
21.57 / .885 / .193
20.54 / .833 / .185
23.55 / .890 / .207
ConeGaussian (isotropic)
27.96 / .921 / .242
28.45 / .932 / .194
27.34 / .937 / .137
27.37 / .938 / .089
27.78 / .932 / .165
ConeGaussian (anisotropic)
27.97 / .922 / .239
28.45 / .932 / .193
28.25 / .942 / .128
27.93 / .942 / .079
28.15 / .934 / .160
Zip-NeRF [1pt]fisheye
3DGEER (baseline)
24.13 / .787 / .409
24.92 / .845 / .278
25.87 / .899 / .150
25.82 / .906 / .104
25.19 / .859 / .235
VKRayGS z/f
23.37 / .786 / .406
24.61 / .846 / .276
26.00 / .902 / .148
25.92 / .913 / .092
24.97 / .862 / .230
Table 2: Zoom-out . Models are trained at native resolution and evaluated at {1,21,41,81} with the STMT setting ( Yu et al. 2024a ) .
Dataset
Method
×1
×2
×4
Avg.
Corner ×4
ScanNet++ [1pt]fisheye
3DGEER (baseline)
27.28 / .944 / .122
27.74 / .918 / .221
27.33 / .895 / .279
27.45 / .919 / .208
22.73 / .871 / –
3DGEER (+ Mip-Splatting floor)
29.49 / .951 / .111
27.19 / .918 / .216
27.25 / .896 / .272
27.98 / .922 / .200
22.40 / .872 / –
3DGEER (+ footprint floor)
28.73 / .949 / .112
28.13 / .921 / .213
27.42 / .897 / .271
28.09 / .922 / .199
22.82 / .879 / –
ConeGaussian (ours)
28.80 / .950 / .111
28.32 / .924 / .204
27.52 / .900 / .264
28.21 / .925 / .193
22.88 / .879 / –
Zip-NeRF [1pt]fisheye
3DGEER (baseline)
25.87 / .899 / .150
24.92 / .845 / .278
24.13 / .787 / .409
24.97 / .844 / .279
21.72 / .780 / –
3DGEER (+ Mip-Splatting floor)
25.88 / .899 / .150
24.92 / .845 / .279
24.13 / .787 / .409
24.98 / .844 / .279
21.73 / .779 / –
Table 3: Zoom-in . Models are trained at the lowest resolution and rendered at ×1 – ×4 magnification. Corner PSNR/SSIM are computed over the normalized radial band r > 0.9.
Figure 3: Qualitative Results. Comparisons across ScanNet++, Zip-NeRF, and Mip-NeRF 360, covering fisheye and pinhole cameras.
Figure 4: Zoom-out Qualitative Results. Highlighted cable and foliage crops compare the baseline, ours, and ground truth.
Setting
Method
1
21
41
81
Avg.
FH → PH [1pt]full img
3DGEER (baseline)
27.42 / .935 / .257
29.99 / .940 / .203
29.66 / .941 / .156
28.34 / .930 / .136
28.85 / .936 / .188
VKRayGS-insp. z/f dilation
26.99 / .933 / .253
28.50 / .937 / .202
25.73 / .919 / .172
23.02 / .871 / .172
26.06 / .915 / .200
ConeGaussian (ours)
27.38 / .934 / .255
30.14 / .942 / .199
30.28 / .949 / .135
29.56 / .951 / .083
29.34 / .944 / .168
PH → FH [1pt]full img
3DGEER (baseline)
27.01 / .919 / .234
27.47 / .931 / .185
27.53 / .936 / .139
26.66 / .923 / .120
27.17 / .927 / .169
ConeGaussian (ours)
27.01 / .920 / .230
27.47 / .931 / .181
27.69 / .942 / .124
27.02 / .939 / .077
27.30 / .933 / .153
PH → FH [1pt]peripheral img
3DGEER (baseline)
23.82 / .892 / –
24.11 / .897 / –
24.32 / .901 / –
24.12 / .890 / –
24.09 / .895 / –
Table 4: Cross-camera rendering on ScanNet++ (FH = fisheye, PH = pinhole). FH → PH models are trained on native fisheye views and rendered through the matched pinhole cameras without retraining; PH → FH evaluates the reverse transfer at the same four scales. Peripheral PSNR/SSIM are computed over the normalized radial band r>0.7 .
Method
×4
×2
×1
21
41
81
ConeGaussian (ours)
27.57 / .900 / .264
28.37 / .924 / .204
28.86 / .950 / .111
28.94 / .959 / .052
29.22 / .960 / .041
27.18 / .949 / .044
additive combination
27.37 / .900 / .264
28.14 / .923 / .205
28.80 / .950 / .110
28.64 / .958 / .053
29.20 / .959 / .041
27.24 / .950 / .044
w/o 3D floor
27.47 / .898 / .272
28.24 / .923 / .207
28.79 / .949 / .113
28.14 / .956 / .056
29.10 / .958 / .044
27.02 / .947 / .046
w/o 2D filter
27.46 / .897 / .271
28.18 / .921 / .213
28.78 / .949 / .112
28.47 / .951 / .069
27.09 / .930 / .092
24.30 / .901 / .096
w/o both (3DGEER)
27.37 / .895 / .279
27.78 / .918 / .221
27.34 / .944 / .122
28.36 / .950 / .074
27.04 / .929 / .095
24.31 / .901 / .096
Table 5: Joint module ablation on ScanNet++ fisheye. Models are trained at 41 native resolution and evaluated from ×4 to 81 relative to the training scale.
Figure 5: Zoom-in Qualitative Results. Highlighted door-handle and wall-light crops compare the baseline, ours, and ground truth.
School of Artificial Intelligence and Robotics, Hunan University, China · National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China