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.
3D Gaussian Splatting has made Gaussian primitives a highly efficient representation for real-time novel view synthesis, but its rasterisation-based formulation relies on screen-space approximations that limit accurate view-dependent ordering and the integration of secondary ray effects such as reflections, refractions, and shadows. Gaussian ray tracing addresses these limitations by evaluating explicit ray-primitive intersections, yet it remains costly to train. We observe that the main bottleneck is not ray traversal alone, but the pixel-centric backward propagation, where many threads concurrently accumulate gradients into the same primitive parameters, causing severe atomic contention and thread serialisation. We present 3DGART, a practical training framework for ray-traced Gaussian rendering. Our key idea is to reorganise backward propagation around primitives rather than pixels. Using conservative perspective-correct screen-space bounds, we build a compact intermediate buffer and a tile-primitive mapping that allows each thread to accumulate the contribution of one primitive over its covered pixels within a tile. This transforms gradient computation from a contention-heavy scatter operation into a structured gather-like process. On Mip-NeRF 360, 3DGART achieves an ≈3−3.5× raw training speedup over per-pixel baseline and ≈4× over 3DGRT on Mip-NeRF 360 while improving quality. More importantly, 3DGART makes fully ray-traced Gaussian training practical, reaching runtimes competitive with rasterisation-based pipelines while preserving benefits of ray tracing.
Existing 3D Gaussian Splatting (3DGS) frameworks rely on camera-specific rasterization, suffering from inconsistent solid-angle sampling and degraded performance across heterogeneous camera models (e.g., perspective, fisheye, omnidirectional). To address this limitation, we propose UniTriSplat, a unified 3DGS framework for universal cameras that reformulates Gaussian splatting on the unit sphere via HEALPix discretization. Leveraging the equal-area property of HEALPix, we construct a spherical sampling grid aligned with the angular resolution of input images. We derive the forward rendering and gradient propagation of Gaussians directly in the spherical radian domain, yielding uniform optimization behavior from narrow-FoV images to full 360-degree panoramas. To enhance perceptual reconstruction quality, we additionally introduce a HEALPix-aware SSIM loss that respects spherical neighborhood structure. Extensive experiments across diverse camera models demonstrate that UniTriSplat consistently improves cross-camera generalization while preserving geometric fidelity and rendering quality.
Yipeng Zhu, Huajian Huang, Tristan Braud +1
The Hong Kong University of Science and Technology, Hong Kong, China · Beijing Institute of Technology, Beijing, China
Omnidirectional images are increasingly used in robotics and vision due to their wide field of view. However, extending 3D Gaussian Splatting (3DGS) to panoramic camera models remains challenging, as existing formulations are designed for perspective projections and naive adaptations often introduce distortion and geometric inconsistencies. We present Spherical-GOF, an omnidirectional Gaussian rendering framework built upon Gaussian Opacity Fields (GOF). Unlike projection-based rasterization, Spherical-GOF performs GOF ray sampling directly on the unit sphere in spherical ray space, enabling consistent ray-Gaussian interactions for panoramic rendering. To make the spherical ray casting efficient and robust, we derive a conservative spherical bounding rule for fast ray-Gaussian culling and introduce a spherical filtering scheme that adapts Gaussian footprints to distortion-varying panoramic pixel sampling. Extensive experiments on standard panoramic benchmarks (OmniBlender and OmniPhotos) demonstrate competitive photometric quality and substantially improved geometric consistency. Compared with the strongest baseline, Spherical-GOF reduces depth reprojection error by 57% and improves cycle inlier ratio by 21%. Qualitative results show cleaner depth and more coherent normal maps, with strong robustness to global panorama rotations. We further validate generalization on OmniRob, a real-world robotic omnidirectional dataset introduced in this work, featuring UAV and quadruped platforms. The source code and the OmniRob dataset will be released at https://github.com/1170632760/Spherical-GOF.
Zhe Yang, Guoqiang Zhao, Sheng Wu +2
School of Artificial Intelligence and Robotics, Hunan University, China · National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China