Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis

Chamuditha Jayanga Galappaththige; Thomas Gottwald; Peter Stehr; Edgar Heinert; Niko Suenderhauf; Dimity Miller; Matthias Rottmann

cs.CVMar 24, 2026

Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis

Authors: Chamuditha Jayanga Galappaththige, Thomas Gottwald, Peter Stehr, Edgar Heinert, Niko Suenderhauf, Dimity Miller, Matthias Rottmann

Organizations: QUT Centre for Robotics, Australia · ARIAM Hub, Australia · University of Wuppertal, Germany · University of Osnabrück, Germany

Abstract

Recent advances in 3D Gaussian Splatting have enabled impressive photorealistic novel view synthesis. However, to transition from a pure rendering engine to a reliable spatial map for autonomous agents and safety-critical applications, knowing where the representation is uncertain is as important as the rendering fidelity itself. We bridge this critical gap by introducing a lightweight, plug-and-play framework for pixel-wise, view-dependent predictive uncertainty estimation. Our post-hoc method formulates uncertainty as a Bayesian-regularized linear least-squares optimization over reconstruction residuals. This architecture-agnostic approach extracts a per-primitive uncertainty channel without modifying the underlying scene representation or degrading baseline visual fidelity. Crucially, we demonstrate that providing this actionable reliability signal successfully translates 3D Gaussian splatting into a trustworthy spatial map, further improving state-of-the-art performance across three critical downstream perception tasks: active view selection, pose-agnostic scene change detection, and pose-agnostic anomaly detection.

Figures & tables

Figure 1: Per-pixel UE for NVS. Our post-hoc method generates a view-dependent uncertainty map that closely mirrors regions of error within the RGB render.

Panel of Figure 2 — Figure 2: Visual example of reconstruction residual on training views. (Left) ground truth, (Center) rendering, (Right) residual map. The residual maps capture errors caused by both imperfect geometry ( e.g . , under-reconstructed regions in vegetation) and the limited capacity of view-dependent appearance ( e.g . , reflections on the table).

Panel of Figure 3 — Figure 3: Overview of our approach: We learn a primitive-level uncertainty channel $u_{k}$ on a well-trained 3DGS, leveraging a (Bayesian-inspired) regularized linear least-squares formulation of training residuals. The learned view-dependent uncertainty can be rasterized to any given (novel) viewpoint $P_{j}$ . Our method is post-hoc and therefore can be seamlessly integrated with any 3DGS variant without loss of fidelity.

Figure 4: Qualitative comparison of predicted uncertainty maps on the bonsai and stump scenes of Mip-NeRF360 [ 4 ] . Our UE more closely reflects the true error when compared to the baselines. Additional qualitative examples can be found in App. F.

Method	Mip-NeRF360 [ 4 ]					Tanks&Temples [ 28 ]					Deep Blending [ 18 ]
	AUSE $(\downarrow)$		Pearson $(\uparrow)$		OH $(\downarrow)$	AUSE $(\downarrow)$		Pearson $(\uparrow)$		OH $(\downarrow)$	AUSE $(\downarrow)$		Pearson $(\uparrow)$		OH $(\downarrow)$
	$L^{1}$	DSSIM	$L^{1}$	DSSIM	( $\%$ )	$L^{1}$	DSSIM	$L^{1}$	DSSIM	( $\%$ )	$L^{1}$	DSSIM	$L^{1}$	DSSIM	( $\%$ )
FisherRF [ 21 ]	0.708	0.606	-0.055	0.009	14.2	0.691	0.709	-0.087	-0.145	19.3	0.751	0.853	-0.116	-0.190	19.7
Manifold [ 37 ]	0.520	0.559	0.070	-0.005	30.2	0.574	0.654	0.053	0.008	23.6	0.503	0.548	0.095	0.074	48.1
Var3DGS [ 33 ]	0.558	0.495	0.118	0.160	$>$ 100	0.539	0.567	0.161	0.176	$>$ 100	0.588	0.671	0.106	0.006	$>$ 100
Ours	0.328	0.214	0.369	0.547	12.8	0.299	0.233	0.427	0.571	14.0	0.376	0.356	0.243	0.244	13.0

Table 1: Quantitative results for UE for NVS. We consistently outperform all baselines across all metrics and datasets while achieving minimal training overhead (OH).

Table 2: Results for (a) spherical harmonics degree ablation and (b) AVS.

Panel of Figure 5 — Figure 5: Highly novel view experiments: (a) example for view setup (b) results

Figure 6: Qualitative examples of uncertainty maps with Bayesian-inspired regularization for different weights $\lambda_{\mathrm{reg}}$ (see Eq. 7 ) in the setting of highly novel views. The given AUSE values are with respect to DSSIM.

Figure 7: Qualitative examples of uncertainty-guided scene change detection. Our predicted uncertainty maps effectively capture rendering artifacts, allowing the system to suppress false positives caused by these artifacts rather than actual scene changes.

	Feature Diff.			MV3DCD-ZS [ 13 ]			MV3DCD [ 13 ]			Online-SCD [ 12 ]
Metric	Base	+ Ours	$\Delta(\%)$	Base	+ Ours	$\Delta(\%)$	Base	+ Ours	$\Delta(\%)$	Base	+ Ours	$\Delta(\%)$
mIoU $(\uparrow)$	0.278	0.359	29.1	0.382	0.439	14.9	0.470	0.498	6.0	0.486	0.498	2.5
F1 $(\uparrow)$	0.402	0.502	24.9	0.526	0.593	12.7	0.621	0.649	4.5	0.638	0.651	2.1

Table 3: Numerical results for pose-agnostic SCD with uncertainty on PASLCD [ 13 ] . Our method consistently improves both state-of-the-art offline and online baselines. Percentage increase over the baseline is given in the

\Delta(\%)

column.

	SplatPose [ 31 ]			SplatPosePlus [ 35 ]
Metric	Base	+ Ours	$\Delta(\%)$	Base	+ Ours	$\Delta(\%)$
AUROC $(\uparrow)$	0.929	0.939	1.1	0.940	0.956	0.7
AUPRO $(\uparrow)$	0.700	0.765	9.3	0.761	0.798	4.9

Table 4: Quantitative results for pose-agnostic AD on MAD-Real [ 62 ] . Our UE guidance enables SplatPose [ 31 ] to match or surpass its successor, SplatPosePlus [ 35 ] . Furthermore, applying our framework directly to SplatPosePlus advances the current state-of-the-art. Percentage increase over the baseline is given in the

\Delta(\%)

column.

Method	AUSE $(\downarrow)$		Pearson $(\uparrow)$
Method	L1	DSSIM	L1	DSSIM
3DGS [ 23 ]
$+$ FisherRF	0.708	0.606	-0.055	0.009
$+$ Ours	0.328	0.214	0.369	0.547
Depth regularized 3DGS [ 24 ]
$+$ FisherRF	0.704	0.603	-0.059	-0.018

Table 5: Quantitative results for UE in NVS on Mip-NeRF360 [ 4 ] with different 3DGS variants [ 24 , 23 , 16 , 61 ] . Our approach can be applied post-hoc to any 3DGS variant and delivers comparable performance across variants.

Method	Mip-NeRF360			Tanks&Temples			Deep Blending
Method	PSNR $(\uparrow)$	SSIM $(\uparrow)$	LPIPS $(\downarrow)$	PSNR $(\uparrow)$	SSIM $(\uparrow)$	LPIPS $(\downarrow)$	PSNR $(\uparrow)$	SSIM $(\uparrow)$	LPIPS $(\downarrow)$
Manifold [ 37 ]	27.273	0.809	0.230	23.517	0.842	0.190	29.239	0.902	0.252
Var3DGS [ 33 ]	27.137	0.807	0.231	23.321	0.840	0.186	28.806	0.895	0.263
3DGS [ 23 ]	27.595	0.816	0.216	23.474	0.848	0.172	29.899	0.908	0.242

Table 6: Reconstruction quality comparison of stochastic 3DGS methods Manifold [ 37 ] and Var3DGS [ 33 ] to standard 3DGS [ 23 ] , which forms the underlying representation for our method and FisherRF [ 21 ] . Corresponding visual examples are shown in Figs. 11 and 12 .

Figure 8: Scene-wise results for uncertainty estimation on highly novel views for the Mip-NeRF360 [ 4 ] scenes bicycle , bonsai , and counter .

Figure 9: Scene-wise results for uncertainty estimation on highly novel views for the Mip-NeRF360 [ 4 ] scenes flowers , garden , and kitchen .

Figure 10: Scene-wise results for uncertainty estimation on highly novel views for the Mip-NeRF360 [ 4 ] scenes room , stump , and treehill .

Panel of Figure 11 — Figure 11: Qualitative comparison of predicted uncertainty maps to DSSIM error map. Including separate renderings and error maps for the stochastic 3DGS methods Manifold [ 37 ] and Var3DGS [ 33 ] . Top: bicycle from Mip-NeRF360 [ 4 ] . Bottom: counter from Mip-NeRF360 [ 4 ] .

Panel of Figure 12 — Figure 12: Qualitative comparison of predicted uncertainty maps to DSSIM error map. Including separate renderings and error maps for the stochastic 3DGS methods Manifold [ 37 ] and Var3DGS [ 33 ] . Top: room from Mip-NeRF360 [ 4 ] . Bottom: truck from Tanks&Temples [ 28 ] .

Iterations $I$	PSNR $(\uparrow)$	SSIM $(\uparrow)$	LPIPS $(\downarrow)$
10	20.316	0.617	0.342
25	20.541	0.618	0.343
50	20.676	0.615	0.344
100	20.604	0.612	0.346
200	20.719	0.617	0.343

Table 7: Quantitative comparison of reconstruction fidelity for different numbers of iterations per view

I

	Blur		Noise		Num. Views
	5%	10%	5%	10%	64	16	4
$\Delta$ unc.	119%	129%	127%	136%	122%	266%	596%

Table 8: Predicted uncertainty level (

\Delta

unc.: increase in mean uncertainty on test views) relative to the standard 3DGS setting, for blur/noise added to a fraction [%] of the training views, or for a reduced number of training views. Results for the bicycle scene of Mip-NeRF360 [ 4 ] . The predicted uncertainty increases monotonically under both aleatoric (blur/noise) and epistemic (fewer views) perturbations.

Figure 13: Qualitative example showing that adding Gaussian noise to the 3DGS training views ( Noise ) increases the estimated uncertainty compared to 3DGS training without noise ( Ref. ).

3D Scene Change Detection

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Jul 6, 2026J/K move · Enter open · S save

Rendering-Aware Bayesian 3D Gaussian Splatting with Native Uncertainty and Adaptive Complexity Control

Gaoxiang Jia, Vikram Appia, Junzhou Huang, Xinlei Wang

Advanced Micro Devices, Inc. · Dept. of Computer Science and Engineering University of Texas at Arlington · Dept. of Mathematics & Division of Data Science University of Texas at Arlington

Active View Selection Active Perception Uncertainty Quantification 3D Gaussian Splatting Sparse-View 3D Reconstruction 3DGS Training+1

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3D Gaussian splatting (3DGS) is a strong representation for real-time novel-view synthesis, but its standard training pipeline relies on point estimates and hand-tuned heuristics, providing no native uncertainty or principled complexity control. This is most limiting under sparse views or fixed acquisition budgets, where a model must identify weakly supported geometry and select informative views. We introduce a rendering-aware Bayesian 3DGS framework that tracks Gaussian geometry with a Normal-Inverse-Wishart posterior over means and covariances using renderer-derived surrogate summaries. An optional Dirichlet-process extension adds a probabilistic component-usage signal, and the training schedule makes the closed-form versus approximate inference boundary explicit. Re-rendering posterior geometry samples yields native predictive uncertainty for interval calibration and active view selection. In a fixed-budget 16-to-32 active-view task, native NIW acquisition improves PSNR by +0.453 dB and LPIPS by -0.0146 over a scoring-only 3-member standard-ensemble baseline, winning 29/39 scene-seed pairs and 10/13 scene means; it also improves over PPU-style (+0.355 dB) and NIW-proxy (+0.401 dB) acquisition. NIW native intervals reduce 95% coverage error by about 17x relative to a shared proxy (0.046 vs. 0.796) and are about 10x closer to nominal coverage than a 3-member deep ensemble (0.047 vs. 0.454) at roughly one-third the training cost. As a reconstruction compatibility check, paired NIW-vs-standard analysis over 39 scene-seed runs yields +0.030 dB PSNR with 1.6% additional training time. These results position Bayesian 3DGS as a practical probabilistic scene representation for decision-facing tasks such as active view selection.

Abstract

3D Scene Change Detection

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